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

Modifier and Type	Field and Description
private final List<WeightedObservedPoint>	observations Observed points.
private final DifferentiableMultivariateVectorOptimizer	oldOptimizer Deprecated as of 3.1 replaced by `optimizer` Optimizer to use for the fitting.
private final MultivariateDifferentiableVectorOptimizer	optimizer Deprecated as a consequence of `MultivariateDifferentiableVectorOptimizer` being deprecated. Optimizer to use for the fitting.

Access	Constructor and Description
public	CurveFitter(final DifferentiableMultivariateVectorOptimizer optimizer to use for the fitting optimizer) Deprecated as of 3.1 replaced by `CurveFitter(MultivariateDifferentiableVectorOptimizer)` Simple constructor.
public	CurveFitter(final MultivariateDifferentiableVectorOptimizer optimizer to use for the fitting optimizer) Deprecated as a consequence of `MultivariateDifferentiableVectorOptimizer` being deprecated. Simple constructor.

Method Summary

Modifier and Type	Method and Description
public void	addObservedPoint(double abscissa of the point x, double observed value of the point at x, after fitting we should have f(x) as close as possible to this value y) Add an observed (x,y) point to the sample with unit weight.
public void	addObservedPoint(double weight of the observed point in the fit weight, double abscissa of the point x, double observed value of the point at x, after fitting we should have f(x) as close as possible to this value y) Add an observed weighted (x,y) point to the sample.
public void	addObservedPoint(WeightedObservedPoint observed point to add observed) Deprecated as a consequence of `WeightedObservedPoint` being deprecated. Add an observed weighted (x,y) point to the sample.
public void	clearObservations() Remove all observations.
public double[]	Returns: the fitted parameters. fit(T parametric function to fit. f, final double[] first guess of the function parameters. initialGuess) Fit a curve.
public double[]	Returns: the fitted parameters. fit(int Maximum number of function evaluations. maxEval, T parametric function to fit. f, final double[] first guess of the function parameters. initialGuess) Fit a curve.
public WeightedObservedPoint[]	Returns: observed points getObservations() Deprecated as a consequence of `WeightedObservedPoint` being deprecated. Get the observed points.

Inherited from java.lang.Object:: clone equals finalize getClass hashCode notify notifyAll toString wait wait wait

Field Detail

observations	back to summary
private final List<WeightedObservedPoint> observations Observed points.

oldOptimizer	back to summary
private final DifferentiableMultivariateVectorOptimizer oldOptimizer Deprecated as of 3.1 replaced by `optimizer` Optimizer to use for the fitting. Annotations @Deprecated

optimizer	back to summary
private final MultivariateDifferentiableVectorOptimizer optimizer Deprecated as a consequence of `MultivariateDifferentiableVectorOptimizer` being deprecated. See corresponding docs for further information. Optimizer to use for the fitting.

Constructor Detail

CurveFitter back to summary

CurveFitter	back to summary
public CurveFitter(final DifferentiableMultivariateVectorOptimizer optimizer) Deprecated as of 3.1 replaced by `CurveFitter(MultivariateDifferentiableVectorOptimizer)` Simple constructor. Parameters optimizer:DifferentiableMultivariateVectorOptimizer optimizer to use for the fitting Annotations @Deprecated

public CurveFitter(final DifferentiableMultivariateVectorOptimizer optimizer)

Deprecated

as of 3.1 replaced by CurveFitter(MultivariateDifferentiableVectorOptimizer)

Simple constructor.

Parameters

optimizer:DifferentiableMultivariateVectorOptimizer: optimizer to use for the fitting

Annotations

@Deprecated

CurveFitter back to summary

CurveFitter	back to summary
public CurveFitter(final MultivariateDifferentiableVectorOptimizer optimizer) Deprecated as a consequence of `MultivariateDifferentiableVectorOptimizer` being deprecated. See corresponding docs for further information. Simple constructor. Parameters optimizer:MultivariateDifferentiableVectorOptimizer optimizer to use for the fitting Since 3.1

public CurveFitter(final MultivariateDifferentiableVectorOptimizer optimizer)

Deprecated

as a consequence of MultivariateDifferentiableVectorOptimizer being deprecated. See corresponding docs for further information.

Simple constructor.

Parameters

optimizer:MultivariateDifferentiableVectorOptimizer: optimizer to use for the fitting

Since

3.1

Method Detail

addObservedPoint back to summary

addObservedPoint	back to summary
public void addObservedPoint(double x, double y) 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:double abscissa of the point y:double 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()`

public void addObservedPoint(double x, double y)

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

addObservedPoint back to summary

addObservedPoint	back to summary
public void addObservedPoint(double weight, double x, double y) Add an observed weighted (x,y) point to the sample. Parameters weight:double weight of the observed point in the fit x:double abscissa of the point y:double 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()`

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

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

Parameters

weight:double: weight of the observed point in the fit
x:double: abscissa of the point
y:double: observed value of the point at x, after fitting we should have f(x) as close as possible to this value

addObservedPoint back to summary

addObservedPoint	back to summary
public void addObservedPoint(WeightedObservedPoint observed) Deprecated as a consequence of `WeightedObservedPoint` being deprecated. See corresponding docs for further information. Add an observed weighted (x,y) point to the sample. Parameters observed:WeightedObservedPoint observed point to add See Also `addObservedPoint(double, double)`, `addObservedPoint(double, double, double)`, `getObservations()`

public void addObservedPoint(WeightedObservedPoint observed)

Deprecated

as a consequence of WeightedObservedPoint being deprecated. See corresponding docs for further information.

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

Parameters

observed:WeightedObservedPoint: observed point to add

clearObservations	back to summary
public void clearObservations() Remove all observations.

fit back to summary

fit	back to summary
public double[] fit(T f, final double[] initialGuess) 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:T parametric function to fit. initialGuess:double[] first guess of the function parameters. Returns:double[] the fitted parameters. Exceptions DimensionMismatchException: if the start point dimension is wrong.

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

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:T: parametric function to fit.
initialGuess:double[]: first guess of the function parameters.

Returns:double[]

the fitted parameters.

Exceptions

DimensionMismatchException:: if the start point dimension is wrong.

fit back to summary

fit	back to summary
public double[] fit(int maxEval, T f, final double[] initialGuess) 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 maxEval:int Maximum number of function evaluations. f:T parametric function to fit. initialGuess:double[] first guess of the function parameters. Returns:double[] the fitted parameters. Exceptions TooManyEvaluationsException: if the number of allowed evaluations is exceeded. DimensionMismatchException: if the start point dimension is wrong. Since 3.0

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

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

maxEval:int: Maximum number of function evaluations.
f:T: parametric function to fit.
initialGuess:double[]: first guess of the function parameters.

Returns:double[]

the fitted parameters.

Exceptions

TooManyEvaluationsException:: if the number of allowed evaluations is exceeded.
DimensionMismatchException:: if the start point dimension is wrong.

Since

3.0

getObservations back to summary

getObservations	back to summary
public WeightedObservedPoint[] getObservations() Deprecated as a consequence of `WeightedObservedPoint` being deprecated. See corresponding docs for further information. Get the observed points. Returns:WeightedObservedPoint[] observed points See Also `addObservedPoint(double, double)`, `addObservedPoint(double, double, double)`, `addObservedPoint(WeightedObservedPoint)`

public WeightedObservedPoint[] getObservations()

Deprecated

as a consequence of WeightedObservedPoint being deprecated. See corresponding docs for further information.

Get the observed points.

Returns:WeightedObservedPoint[]: observed points
See Also: addObservedPoint(double, double), addObservedPoint(double, double, double), addObservedPoint(WeightedObservedPoint)

org.apache.commons.math3.optimization.fitting back to summary

private Class CurveFitter.OldTheoreticalValuesFunction

Class Inheritance

extends Object
implements DifferentiableMultivariateVectorFunction

java.lang.Object
org.apache.commons.math3.optimization.fitting.CurveFitter.OldTheoreticalValuesFunction

All Implemented Interfaces

org.apache.commons.math3.analysis.DifferentiableMultivariateVectorFunction, org.apache.commons.math3.analysis.MultivariateVectorFunction

Annotations

@Deprecated

Deprecated

Vectorial function computing function theoretical values.

Field Summary

Modifier and Type	Field and Description
private final ParametricUnivariateFunction	f Function to fit.

Constructor Summary

Access	Constructor and Description
pack-priv	OldTheoreticalValuesFunction(final ParametricUnivariateFunction function to fit. f) Simple constructor.

Method Summary

Modifier and Type	Method and Description
public MultivariateMatrixFunction	jacobian() Implements org.apache.commons.math3.analysis.DifferentiableMultivariateVectorFunction.jacobian. Returns the jacobian function.
public double[]	value(double[] point at which the function must be evaluated point) Implements org.apache.commons.math3.analysis.MultivariateVectorFunction.value. Compute the value for the function at the given point.

Inherited from java.lang.Object:: clone equals finalize getClass hashCode notify notifyAll toString wait wait wait

Field Detail

f	back to summary
private final ParametricUnivariateFunction f Function to fit.

Constructor Detail

OldTheoreticalValuesFunction	back to summary
pack-priv OldTheoreticalValuesFunction(final ParametricUnivariateFunction f) Simple constructor. Parameters f:ParametricUnivariateFunction function to fit.

OldTheoreticalValuesFunction

back to summary

pack-priv OldTheoreticalValuesFunction(final ParametricUnivariateFunction f)

Simple constructor.

Parameters

f:ParametricUnivariateFunction: function to fit.

Method Detail

jacobian	back to summary
public MultivariateMatrixFunction jacobian() Implements org.apache.commons.math3.analysis.DifferentiableMultivariateVectorFunction.jacobian. Doc from org.apache.commons.math3.analysis.DifferentiableMultivariateVectorFunction.jacobian. Returns the jacobian function. Returns:MultivariateMatrixFunction the jacobian function

jacobian

back to summary

public MultivariateMatrixFunction jacobian()

Implements org.apache.commons.math3.analysis.DifferentiableMultivariateVectorFunction.jacobian.

Doc from org.apache.commons.math3.analysis.DifferentiableMultivariateVectorFunction.jacobian.

Returns the jacobian function.

Returns:MultivariateMatrixFunction: the jacobian function

value	back to summary
public double[] value(double[] point) Implements org.apache.commons.math3.analysis.MultivariateVectorFunction.value. Doc from org.apache.commons.math3.analysis.MultivariateVectorFunction.value. Compute the value for the function at the given point. Parameters point:double[] point at which the function must be evaluated Returns:double[] function value for the given point

value

back to summary

public double[] value(double[] point)

Implements org.apache.commons.math3.analysis.MultivariateVectorFunction.value.

Doc from org.apache.commons.math3.analysis.MultivariateVectorFunction.value.

Compute the value for the function at the given point.

Parameters

point:double[]: point at which the function must be evaluated

Returns:double[]

function value for the given point

org.apache.commons.math3.optimization.fitting back to summary

private Class CurveFitter.TheoreticalValuesFunction

Class Inheritance

extends Object
implements MultivariateDifferentiableVectorFunction

java.lang.Object
org.apache.commons.math3.optimization.fitting.CurveFitter.TheoreticalValuesFunction

All Implemented Interfaces

org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction, org.apache.commons.math3.analysis.MultivariateVectorFunction

Vectorial function computing function theoretical values.

Field Summary

Modifier and Type	Field and Description
private final ParametricUnivariateFunction	f Function to fit.

Constructor Summary

Access	Constructor and Description
pack-priv	TheoreticalValuesFunction(final ParametricUnivariateFunction function to fit. f) Simple constructor.

Method Summary

Modifier and Type	Method and Description
public double[]	value(double[] point at which the function must be evaluated point) Implements org.apache.commons.math3.analysis.MultivariateVectorFunction.value. Compute the value for the function at the given point.
public DerivativeStructure[]	value(DerivativeStructure[] point at which the function must be evaluated point) Implements org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction.value. Compute the value for the function at the given point.

Inherited from java.lang.Object:: clone equals finalize getClass hashCode notify notifyAll toString wait wait wait

Field Detail

f	back to summary
private final ParametricUnivariateFunction f Function to fit.

Constructor Detail

TheoreticalValuesFunction	back to summary
pack-priv TheoreticalValuesFunction(final ParametricUnivariateFunction f) Simple constructor. Parameters f:ParametricUnivariateFunction function to fit.

TheoreticalValuesFunction

back to summary

pack-priv TheoreticalValuesFunction(final ParametricUnivariateFunction f)

Simple constructor.

Parameters

f:ParametricUnivariateFunction: function to fit.

Method Detail

value	back to summary
public double[] value(double[] point) Implements org.apache.commons.math3.analysis.MultivariateVectorFunction.value. Doc from org.apache.commons.math3.analysis.MultivariateVectorFunction.value. Compute the value for the function at the given point. Parameters point:double[] point at which the function must be evaluated Returns:double[] function value for the given point

value

back to summary

public double[] value(double[] point)

Implements org.apache.commons.math3.analysis.MultivariateVectorFunction.value.

Doc from org.apache.commons.math3.analysis.MultivariateVectorFunction.value.

Compute the value for the function at the given point.

Parameters

point:double[]: point at which the function must be evaluated

Returns:double[]

function value for the given point

value	back to summary
public DerivativeStructure[] value(DerivativeStructure[] point) Implements org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction.value. Doc from org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction.value. Compute the value for the function at the given point. Parameters point:DerivativeStructure[] point at which the function must be evaluated Returns:DerivativeStructure[] function value for the given point

value

back to summary

public DerivativeStructure[] value(DerivativeStructure[] point)

Implements org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction.value.

Doc from org.apache.commons.math3.analysis.differentiation.MultivariateDifferentiableVectorFunction.value.

Compute the value for the function at the given point.

Parameters

point:DerivativeStructure[]: point at which the function must be evaluated

Returns:DerivativeStructure[]

function value for the given point

public Class CurveFitter<T extends ParametricUnivariateFunction>

Nested and Inner Type Summary

Field Summary

Constructor Summary

Method Summary

Field Detail

Constructor Detail

Method Detail

private Class CurveFitter.OldTheoreticalValuesFunction

Field Summary

Constructor Summary

Method Summary

Field Detail

Constructor Detail

Method Detail

private Class CurveFitter.TheoreticalValuesFunction

Field Summary

Constructor Summary

Method Summary

Field Detail

Constructor Detail

Method Detail