Package org.hipparchus.analysis.function

Class Sigmoid.Parametric

java.lang.Object

org.hipparchus.analysis.function.Sigmoid.Parametric

All Implemented Interfaces:

ParametricUnivariateFunction

Enclosing class:

Sigmoid

public static class Sigmoid.Parametric
extends Object
implements ParametricUnivariateFunction

Parametric function where the input array contains the parameters of the sigmoid function, ordered as follows:

• Lower asymptote
• Higher asymptote

Constructor Summary

Constructors

Constructor	Description
Parametric()	Empty constructor.

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 sigmoid at x.

Methods inherited from class java.lang.Object

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

Constructor Details

Parametric

public Parametric()

Empty constructor.

This constructor is not strictly necessary, but it prevents spurious javadoc warnings with JDK 18 and later.

Since:

3.0

Method Details

value

public double value(double x,
 double... param)
             throws MathIllegalArgumentException,
NullArgumentException

Computes the value of the sigmoid at x.

Specified by:

value in interface ParametricUnivariateFunction

Parameters:

x - Value for which the function must be computed.

param - Values of lower asymptote and higher asymptote.

Returns:

the value of the function.

Throws:

NullArgumentException - if param is null.

MathIllegalArgumentException - if the size of param is not 2.

gradient

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

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 (lower asymptote and higher asymptote).

Specified by:

gradient in interface ParametricUnivariateFunction

Parameters:

x - Value at which the gradient must be computed.

param - Values for lower asymptote and higher asymptote.

Returns:

the gradient vector at x.

Throws:

NullArgumentException - if param is null.

MathIllegalArgumentException - if the size of param is not 2.