org.apache.commons.math3.analysis.function

Class Logistic.Parametric

java.lang.Object

org.apache.commons.math3.analysis.function.Logistic.Parametric

All Implemented Interfaces:

ParametricUnivariateFunction

Enclosing class:

Logistic

public static class Logistic.Parametric
extends Object
implements ParametricUnivariateFunction

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

• k
• m
• b
• q
• a
• n

Constructor Summary

Constructors

Constructor and Description
Parametric()

Method Summary

Modifier and Type	Method and 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

equals, 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 sigmoid at x.

Specified by:

value in interface ParametricUnivariateFunction

Parameters:

x - Value for which the function must be computed.

param - Values for k, m, b, q, a and n.

Returns:

the value of the function.

Throws:

NullArgumentException - if param is null.

DimensionMismatchException - if the size of param is not 6.

NotStrictlyPositiveException - if param[5] <= 0.

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.

Specified by:

gradient in interface ParametricUnivariateFunction

Parameters:

x - Value at which the gradient must be computed.

param - Values for k, m, b, q, a and n.

Returns:

the gradient vector at x.

Throws:

NullArgumentException - if param is null.

DimensionMismatchException - if the size of param is not 6.

NotStrictlyPositiveException - if param[5] <= 0.