Package edu.cnu.mdi.splot.fit

Class ErfFitter

java.lang.Object

edu.cnu.mdi.splot.fit.ALeastSquaresFitter

edu.cnu.mdi.splot.fit.ErfFitter

All Implemented Interfaces:

IFitStringGetter, IFitter

public final class ErfFitter
extends ALeastSquaresFitter

Nonlinear least-squares fitter for scaled/shifted erf:

 y(x) = A * erf((x - x0) / sigma) + B
 

Parameter ordering

• params[0] = A
• params[1] = x0
• params[2] = sigma
• params[3] = B

Enforces sigma >= DEFAULT_MIN_SIGMA by default.

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static final class	ErfFitter.ParameterBounds	Bounds container for erf parameters.

Field Summary

Fields

Modifier and Type	Field	Description
static final double	DEFAULT_MIN_SIGMA	Default minimum allowed sigma.
static final int	IDX_A	Parameter indices.
static final int	IDX_B
static final int	IDX_SIGMA
static final int	IDX_X0
static final String[]	paramNames	Base Parameter names.

Fields inherited from class edu.cnu.mdi.splot.fit.ALeastSquaresFitter

CAPSIG, checker, covarianceThreshold, initialGuesser, maxEvaluations, maxIterations, MU, OMEGA, optimizer, PHI, SMALLSIG, SUB0, SUB1, SUB2, subArray, SUBN, SUP0, SUP1, SUP2, SUPN

Constructor Summary

Constructors

Constructor	Description
ErfFitter()	Create an Erf fitter with default optimizer (Levenberg-Marquardt).
ErfFitter(org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer optimizer)	Create an Erf fitter with the given optimizer.

Method Summary

Modifier and Type	Method	Description
Evaluator	asEvaluator(FitResult fit)	Create an evaluator for the fit result.
protected double[]	defaultInitialGuess(double[] x, double[] y, double[] weights)	Default initial guess used if the caller does not provide an explicit override.
protected org.apache.commons.math3.fitting.leastsquares.ParameterValidator	defaultValidator()	Default validator/bounds policy (optional).
FitResult	fit(double[] x, double[] y, double[] weights, ErfFitter.ParameterBounds bounds, double[] initialGuess)	Expert overload: fit with optional weights, optional bounds, and optional initial guess.
String	functionForm()
protected int	getParameterCount()
IFitStringGetter	getStringGetter()	Get the descriptive string getter for this fitter.
protected org.apache.commons.math3.fitting.leastsquares.MultivariateJacobianFunction	model(double[] x)	Build the model + analytic (or numeric) Jacobian for this fitter, given x values.
String	modelName()
String	parameterName(int index)	Get the parameter name for the given index.

Methods inherited from class edu.cnu.mdi.splot.fit.ALeastSquaresFitter

buildFitResult, clampingValidator, fit, fit, fit, getChecker, getCovarianceThreshold, getMaxEvaluations, getMaxIterations, safeCovariances, validateWeights, validateXY, weightsFromSigmaY

Methods inherited from class java.lang.Object

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

Field Details

IDX_A

public static final int IDX_A

Parameter indices.

See Also:

• Constant Field Values

IDX_X0

public static final int IDX_X0

See Also:

• Constant Field Values

IDX_SIGMA

public static final int IDX_SIGMA

See Also:

• Constant Field Values

IDX_B

public static final int IDX_B

See Also:

• Constant Field Values

paramNames

public static final String[] paramNames

Base Parameter names.

DEFAULT_MIN_SIGMA

public static final double DEFAULT_MIN_SIGMA

Default minimum allowed sigma.

See Also:

• Constant Field Values

Constructor Details

ErfFitter

public ErfFitter()

Create an Erf fitter with default optimizer (Levenberg-Marquardt).

ErfFitter

public ErfFitter(org.apache.commons.math3.fitting.leastsquares.LeastSquaresOptimizer optimizer)

Create an Erf fitter with the given optimizer.

Parameters:

optimizer - the least-squares optimizer to use

Method Details

getParameterCount

protected int getParameterCount()

Specified by:

getParameterCount in class ALeastSquaresFitter

Returns:

number of parameters for this fitter.

model

protected org.apache.commons.math3.fitting.leastsquares.MultivariateJacobianFunction model(double[] x)

Description copied from class: ALeastSquaresFitter

Build the model + analytic (or numeric) Jacobian for this fitter, given x values.

Specified by:

model in class ALeastSquaresFitter

Parameters:

x - x data (length n)

Returns:

a MultivariateJacobianFunction compatible with Commons Math least-squares

defaultInitialGuess

protected double[] defaultInitialGuess(double[] x,
 double[] y,
 double[] weights)

Description copied from class: ALeastSquaresFitter

Default initial guess used if the caller does not provide an explicit override. The default implementation delegates to the ALeastSquaresFitter.initialGuesser.

Subclasses may override if they have a better model-specific guess.

Overrides:

defaultInitialGuess in class ALeastSquaresFitter

Parameters:

x - x data

y - y data

weights - optional weights (may be null)

Returns:

initial parameter vector of length ALeastSquaresFitter.getParameterCount()

defaultValidator

protected org.apache.commons.math3.fitting.leastsquares.ParameterValidator defaultValidator()

Description copied from class: ALeastSquaresFitter

Default validator/bounds policy (optional).

Return null for "no validation". For simple bound constraints, consider using ALeastSquaresFitter.clampingValidator(double[], double[]).

Overrides:

defaultValidator in class ALeastSquaresFitter

Returns:

a parameter validator or null

fit

public FitResult fit(double[] x,
 double[] y,
 double[] weights,
 ErfFitter.ParameterBounds bounds,
 double[] initialGuess)

Expert overload: fit with optional weights, optional bounds, and optional initial guess.

Parameters:

x - x data

y - y data

weights - optional weights (typically 1/sigmaY^2), may be null

bounds - optional bounds, may be null

initialGuess - optional initial guess, may be null

Returns:

fit result

asEvaluator

public Evaluator asEvaluator(FitResult fit)

Description copied from class: ALeastSquaresFitter

Create an evaluator for the fit result.

Specified by:

asEvaluator in class ALeastSquaresFitter

Parameters:

fit - the fit result

Returns:

an evaluator

modelName

public String modelName()

Returns:

model name suitable for a legend or report

functionForm

public String functionForm()

Returns:

mathematical function form suitable for display

parameterName

public String parameterName(int index)

Get the parameter name for the given index.

Parameters:

index - the parameter index

Returns:

the parameter name

getStringGetter

public IFitStringGetter getStringGetter()

Description copied from class: ALeastSquaresFitter

Get the descriptive string getter for this fitter.

Specified by:

getStringGetter in class ALeastSquaresFitter

Returns:

the string getter