public class PolynomialFitter extends AbstractCurveFitter
Polynomial fitting is a very simple case of curve fitting. The estimated coefficients are the polynom coefficients. They are searched by a least square estimator.
This class
PolynomialCoefficient,
Serialized FormAbstractCurveFitter.FitMeasurementcoefficients, measurements| Constructor and Description |
|---|
PolynomialFitter(int degree,
Estimator estimator)
Simple constructor.
|
PolynomialFitter(int degree,
int maxIterations,
double convergence,
double steadyStateThreshold,
double epsilon)
Deprecated.
replaced by
PolynomialFitter(int,Estimator)
as of version 7.0 |
PolynomialFitter(PolynomialCoefficient[] coefficients,
Estimator estimator)
Simple constructor.
|
PolynomialFitter(PolynomialCoefficient[] coefficients,
int maxIterations,
double convergence,
double steadyStateThreshold,
double epsilon)
Deprecated.
replaced by
PolynomialFitter(PolynomialCoefficient[],
Estimator) as of version 7.0 |
| Modifier and Type | Method and Description |
|---|---|
double |
partial(double x,
EstimatedParameter p)
Get the derivative of the function at x with respect to parameter p.
|
double |
valueAt(double x)
Get the value of the function at x according to the current parameters value.
|
addWeightedPair, fit, getAllParameters, getMeasurements, getUnboundParameters, sortMeasurementspublic PolynomialFitter(int degree,
Estimator estimator)
The polynomial fitter built this way are complete polynoms,
ie. a n-degree polynom has n+1 coefficients. In order to build
fitter for sparse polynoms (for example a x^20 - b
x^30, on should first build the coefficients array and
provide it to PolynomialFitter(PolynomialCoefficient[], int, double, double,
double).
degree - maximal degree of the polynomestimator - estimator to use for the fittingpublic PolynomialFitter(PolynomialCoefficient[] coefficients, Estimator estimator)
This constructor can be used either when a first estimate of
the coefficients is already known (which is of little interest
because the fit cost is the same whether a first guess is known or
not) or when one needs to handle sparse polynoms like a
x^20 - b x^30.
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.estimator - estimator to use for the fittingpublic PolynomialFitter(int degree,
int maxIterations,
double convergence,
double steadyStateThreshold,
double epsilon)
PolynomialFitter(int,Estimator)
as of version 7.0The polynomial fitter built this way are complete polynoms,
ie. a n-degree polynom has n+1 coefficients. In order to build
fitter for sparse polynoms (for example a x^20 - b
x^30, on should first build the coefficients array and
provide it to PolynomialFitter(PolynomialCoefficient[], int, double, double,
double).
degree - maximal degree of the polynommaxIterations - maximum number of iterations allowedconvergence - criterion threshold below which we do not need
to improve the criterion anymoresteadyStateThreshold - 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).public PolynomialFitter(PolynomialCoefficient[] coefficients, int maxIterations, double convergence, double steadyStateThreshold, double epsilon)
PolynomialFitter(PolynomialCoefficient[],
Estimator) as of version 7.0This constructor can be used either when a first estimate of
the coefficients is already known (which is of little interest
because the fit cost is the same whether a first guess is known or
not) or when one needs to handle sparse polynoms like a
x^20 - b x^30.
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 allowedconvergence - criterion threshold below which we do not need
to improve the criterion anymoresteadyStateThreshold - 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).public double valueAt(double x)
valueAt in class AbstractCurveFitterx - abscissa at which the theoretical value is requestedpublic double partial(double x,
EstimatedParameter p)
partial in class AbstractCurveFitterx - abscissa at which the partial derivative is requestedp - parameter with respect to which the derivative is requestedCopyright © 2001-2007 Luc Maisonobe. All Rights Reserved.