## 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

All Methods
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.

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.