Package org.apache.commons.math4.legacy.analysis.function

Class Gaussian.Parametric

java.lang.Object

org.apache.commons.math4.legacy.analysis.function.Gaussian.Parametric

All Implemented Interfaces:

ParametricUnivariateFunction

Enclosing class:

Gaussian

public static class Gaussian.Parametric
extends Object
implements ParametricUnivariateFunction

Parametric function where the input array contains the parameters of the Gaussian. Ordered as follows:

• Norm
• Mean
• Standard deviation

Constructor Summary

Constructors

Constructor	Description
Parametric()

Method Summary

Modifier and Type	Method	Description
double[]	gradient(double x, double... param)	Computes the value of the gradient at x.
double	value(double x, double... param)	Computes the value of the Gaussian at x.

Methods inherited from class java.lang.Object

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

Constructor Detail

Parametric

public Parametric()

Method Detail

value

public double value(double x,
                    double... param)
             throws NullArgumentException,
                    DimensionMismatchException,
                    NotStrictlyPositiveException

Computes the value of the Gaussian at x.

Specified by:

value in interface ParametricUnivariateFunction

Parameters:

x - Value for which the function must be computed.

param - Values of norm, mean and standard deviation.

Returns:

the value of the function.

Throws:

NullArgumentException - if param is null.

DimensionMismatchException - if the size of param is not 3.

NotStrictlyPositiveException - if param[2] is negative.

gradient

public double[] gradient(double x,
                         double... param)
                  throws NullArgumentException,
                         DimensionMismatchException,
                         NotStrictlyPositiveException

Computes the value of the gradient at x. The components of the gradient vector are the partial derivatives of the function with respect to each of the parameters (norm, mean and standard deviation).

Specified by:

gradient in interface ParametricUnivariateFunction

Parameters:

x - Value at which the gradient must be computed.

param - Values of norm, mean and standard deviation.

Returns:

the gradient vector at x.

Throws:

NullArgumentException - if param is null.

DimensionMismatchException - if the size of param is not 3.

NotStrictlyPositiveException - if param[2] is negative.