org.jscience.mathematics.analysis.fitting
Class AbstractCurveFitter

java.lang.Object
  org.jscience.mathematics.analysis.fitting.AbstractCurveFitter

All Implemented Interfaces:

java.io.Serializable, EstimationProblem

Direct Known Subclasses:

HarmonicFitter, PolynomialFitter

public abstract class AbstractCurveFitter
extends java.lang.Object
implements EstimationProblem, java.io.Serializable

This class is the base class for all curve fitting classes in the package.

This class handles all common features of curve fitting like the sample points handling. It declares two methods (valueAt(double) and partial(double, org.jscience.mathematics.analysis.estimation.EstimatedParameter)) which should be implemented by sub-classes to define the precise shape of the curve they represent.

See Also:

Serialized Form

Nested Class Summary

class

AbstractCurveFitter.FitMeasurement This class represents the fit measurements.

Field Summary

protected EstimatedParameter[]	coefficients Coefficients of the function
protected java.util.List	measurements Measurements vector

Constructor Summary

protected	AbstractCurveFitter(EstimatedParameter[] coefficients, int maxIterations, double convergence, double steadyStateThreshold, double epsilon) Simple constructor.
protected	AbstractCurveFitter(int n, int maxIterations, double convergence, double steadyStateThreshold, double epsilon) Simple constructor.

Method Summary

void	addWeightedPair(double weight, double x, double y) Add a weighted (x,y) pair to the sample.
double[]	fit() Perform the fitting.
EstimatedParameter[]	getAllParameters() Get all the parameters of the problem.
WeightedMeasurement[]	getMeasurements() Get the measurements of an estimation problem.
EstimatedParameter[]	getUnboundParameters() Get the unbound parameters of the problem.
abstract double	partial(double x, EstimatedParameter p) Get the derivative of the function at x with respect to parameter p.
protected void	sortMeasurements() Utility method to sort the measurements with respect to the abscissa.
abstract double	valueAt(double x) Get the value of the function at x according to the current parameters value.

Methods inherited from class java.lang.Object

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

Field Detail

coefficients

protected EstimatedParameter[] coefficients

Coefficients of the function

measurements

protected java.util.List measurements

Measurements vector

Constructor Detail

AbstractCurveFitter

protected AbstractCurveFitter(int n,
                              int maxIterations,
                              double convergence,
                              double steadyStateThreshold,
                              double epsilon)

Simple constructor.

Parameters:

n - number of coefficients in the underlying function

maxIterations - maximum number of iterations allowed

convergence - criterion threshold below which we do not need to improve the criterion anymore

steadyStateThreshold - steady state detection threshold, the problem has reached a steady state (read converged) if Math.abs (Jn - Jn-1) < Jn * convergence, where Jn and Jn-1 are the current and preceding criterion value (square sum of the weighted residuals of considered measurements).

epsilon - threshold under which the matrix of the linearized problem is considered singular (see SquareMatrix.solve).

AbstractCurveFitter

protected AbstractCurveFitter(EstimatedParameter[] coefficients,
                              int maxIterations,
                              double convergence,
                              double steadyStateThreshold,
                              double epsilon)

Simple constructor.

Parameters:

coefficients - first estimate of the coefficients. A reference to this array is hold by the newly created object. Its elements will be adjusted during the fitting process and they will be set to the adjusted coefficients at the end.

maxIterations - maximum number of iterations allowed

convergence - criterion threshold below which we do not need to improve the criterion anymore

steadyStateThreshold - steady state detection threshold, the problem has reached a steady state (read converged) if Math.abs (Jn - Jn-1) < Jn * convergence, where Jn and Jn-1 are the current and preceding criterion value (square sum of the weighted residuals of considered measurements).

epsilon - threshold under which the matrix of the linearized problem is considered singular (see SquareMatrix.solve).

Method Detail

addWeightedPair

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

Add a weighted (x,y) pair to the sample.

Parameters:

weight - weight of this pair in the fit

x - abscissa

y - ordinate, we have y = f (x)

fit

public double[] fit()
             throws EstimationException

Perform the fitting.

This method compute the coefficients of the curve that best fit the sample of weighted pairs previously given through calls to the addWeightedPair method.

Returns:

coefficients of the curve

Throws:

EstimationException - if the fitting is not possible (for example if the sample has to few independant points)

getMeasurements

public WeightedMeasurement[] getMeasurements()

Description copied from interface: EstimationProblem

Get the measurements of an estimation problem.

Specified by:

getMeasurements in interface EstimationProblem

Returns:

measurements

getUnboundParameters

public EstimatedParameter[] getUnboundParameters()

Get the unbound parameters of the problem. For a curve fitting, none of the function coefficient is bound.

Specified by:

getUnboundParameters in interface EstimationProblem

Returns:

unbound parameters

getAllParameters

public EstimatedParameter[] getAllParameters()

Get all the parameters of the problem.

Specified by:

getAllParameters in interface EstimationProblem

Returns:

parameters

sortMeasurements

protected void sortMeasurements()

Utility method to sort the measurements with respect to the abscissa.

This method is provided as a utility for derived classes. As an example, the HarmonicFitter class needs it in order to compute a first guess of the coefficients to initialize the estimation algorithm.

valueAt

public abstract double valueAt(double x)

Get the value of the function at x according to the current parameters value.

Parameters:

x - abscissa at which the theoretical value is requested

Returns:

theoretical value at x

partial

public abstract double partial(double x,
                               EstimatedParameter p)

Get the derivative of the function at x with respect to parameter p.

Parameters:

x - abscissa at which the partial derivative is requested

p - parameter with respect to which the derivative is requested

Returns:

partial derivative