Package com.irurueta.numerical.fitting

Class SvdSingleDimensionLinearFitter

java.lang.Object

com.irurueta.numerical.fitting.Fitter

com.irurueta.numerical.fitting.SingleDimensionFitter

com.irurueta.numerical.fitting.SingleDimensionLinearFitter

com.irurueta.numerical.fitting.SvdSingleDimensionLinearFitter

public class SvdSingleDimensionLinearFitter
extends SingleDimensionLinearFitter

Fits provided data (x,y) to a function made of a linear combination of functions used as a basis (i.e. f(x) = a * f0(x) + b * f1(x) + ...). Where f0, f1, ... is the function basis which ideally should be formed by orthogonal function. This is an improvement on SimpleLinearFitter for cases where basis functions cannot be clearly distinguished from provided data, to avoid matrix singularities and obtain better results. This class is based on the implementation available at Numerical Recipes 3rd Ed, page 795.

Field Summary

Fields

Modifier and Type	Field	Description
static final double	DEFAULT_TOL	Default tolerance.
private double	tol	Tolerance to define convergence threshold for SVD.

Fields inherited from class com.irurueta.numerical.fitting.SingleDimensionLinearFitter

afunc, evaluator, ma

Fields inherited from class com.irurueta.numerical.fitting.SingleDimensionFitter

a, chisq, covar, ndat, sig, x, y

Fields inherited from class com.irurueta.numerical.fitting.Fitter

resultAvailable

Constructor Summary

Constructors

Constructor	Description
SvdSingleDimensionLinearFitter()	Constructor.
SvdSingleDimensionLinearFitter(double[] x, double[] y, double sig)	Constructor.
SvdSingleDimensionLinearFitter(double[] x, double[] y, double[] sig)	Constructor.
SvdSingleDimensionLinearFitter(LinearFitterSingleDimensionFunctionEvaluator evaluator)	Constructor.
SvdSingleDimensionLinearFitter(LinearFitterSingleDimensionFunctionEvaluator evaluator, double[] x, double[] y, double sig)	Constructor.
SvdSingleDimensionLinearFitter(LinearFitterSingleDimensionFunctionEvaluator evaluator, double[] x, double[] y, double[] sig)	Constructor.

Method Summary

Modifier and Type	Method	Description
void	fit()	Fits a function to provided data so that parameters associated to that function can be estimated along with their covariance matrix and chi square value.
double	getTol()	Returns tolerance to define convergence threshold for SVD.
void	setTol(double tol)	Sets tolerance to define convergence threshold for SVD.

Methods inherited from class com.irurueta.numerical.fitting.SingleDimensionLinearFitter

getFunctionEvaluator, isReady, setFunctionEvaluator

Methods inherited from class com.irurueta.numerical.fitting.SingleDimensionFitter

getA, getChisq, getCovar, getSig, getX, getY, setInputData, setInputData

Methods inherited from class com.irurueta.numerical.fitting.Fitter

isResultAvailable

Methods inherited from class java.lang.Object

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

Field Details

DEFAULT_TOL

public static final double DEFAULT_TOL

Default tolerance.

See Also:

• Constant Field Values

tol

private double tol

Tolerance to define convergence threshold for SVD.

Constructor Details

SvdSingleDimensionLinearFitter

public SvdSingleDimensionLinearFitter()

Constructor.

SvdSingleDimensionLinearFitter

public SvdSingleDimensionLinearFitter(double[] x,
 double[] y,
 double[] sig)

Constructor.

Parameters:

x - input points x where a linear single dimensional function f(x) = a * f0(x) + b * f1(x) + ...

y - result of evaluation of linear single dimensional function f(x) at provided x points.

sig - standard deviations of each pair of points (x, y).

Throws:

IllegalArgumentException - if provided arrays don't have the same length.

SvdSingleDimensionLinearFitter

public SvdSingleDimensionLinearFitter(double[] x,
 double[] y,
 double sig)

Constructor.

Parameters:

x - input points x where a linear single dimensional function f(x) = a * f0(x) + b * f1(x) + ...

y - result of evaluation of linear single dimensional function f(x) at provided x points.

sig - standard deviation of all pair of points assuming that standard deviations are constant.

Throws:

IllegalArgumentException - if provided arrays don't have the same length.

SvdSingleDimensionLinearFitter

public SvdSingleDimensionLinearFitter(LinearFitterSingleDimensionFunctionEvaluator evaluator)
                               throws FittingException

Constructor.

Parameters:

evaluator - evaluator to evaluate function at provided point and obtain the evaluation of function basis at such point.

Throws:

FittingException - if evaluation fails.

SvdSingleDimensionLinearFitter

public SvdSingleDimensionLinearFitter(LinearFitterSingleDimensionFunctionEvaluator evaluator,
 double[] x,
 double[] y,
 double[] sig)
                               throws FittingException

Constructor.

Parameters:

evaluator - evaluator to evaluate function at provided point and obtain the evaluation of function basis at such point.

x - input points x where a linear single dimensional function f(x) = a * f0(x) + b * f1(x) + ...

y - result of evaluation of linear single dimensional function f(x) at provided x points.

sig - standard deviation of all pair of points assuming that standard deviations are constant.

Throws:

FittingException - if evaluation fails.

IllegalArgumentException - if provided arrays don't have the same length.

SvdSingleDimensionLinearFitter

public SvdSingleDimensionLinearFitter(LinearFitterSingleDimensionFunctionEvaluator evaluator,
 double[] x,
 double[] y,
 double sig)
                               throws FittingException

Constructor.

Parameters:

evaluator - evaluator to evaluate function at provided point and obtain the evaluation of function basis at such point.

x - input points x where a linear single dimensional function f(x) = a * f0(x) + b * f1(x) + ...

y - result of evaluation of linear single dimensional function f(x) at provided x points.

sig - standard deviation of all pair of points assuming that standard deviations are constant.

Throws:

FittingException - if evaluation fails.

IllegalArgumentException - if provided arrays don't have the same length.

Method Details

getTol

public double getTol()

Returns tolerance to define convergence threshold for SVD.

Returns:

tolerance to define convergence threshold for SVD.

setTol

public void setTol(double tol)

Sets tolerance to define convergence threshold for SVD.

Parameters:

tol - tolerance to define convergence threshold for SVD.

fit

public void fit()
         throws FittingException,
NotReadyException

Fits a function to provided data so that parameters associated to that function can be estimated along with their covariance matrix and chi square value.

Specified by:

fit in class Fitter

Throws:

FittingException - if fitting fails.

NotReadyException - if enough input data has not yet been provided.