joptima.functions

Class ConstrainableFunction

java.lang.Object

joptima.functions.ConstrainableFunction

All Implemented Interfaces:

Function

Direct Known Subclasses:

Arctangent, Cosine, Cubic, Gaussian, Linear, Quadratic, Sine, Tangent

public abstract class ConstrainableFunction
extends java.lang.Object
implements Function

A function that can make parameters stick to given values.

Constructor Summary

Constructors

Constructor and Description
ConstrainableFunction()

Method Summary

Modifier and Type	Method and Description
abstract void	constrain(java.lang.String parametername) Removes the given parameter from the parameter list.
void	constrain(java.lang.String parametername, double value, double error) Remembers a value and error bar to constrain the given parameter to.
void	constrain(java.lang.String parametername, double value, double pluserror, double minuserror) Remembers a value and error bars to constrain the given parameter to.

Methods inherited from class java.lang.Object

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

Methods inherited from interface joptima.Function

calculate, getName, getParameters, setName

Constructor Detail

ConstrainableFunction

public ConstrainableFunction()

Method Detail

constrain

public abstract void constrain(java.lang.String parametername)

Removes the given parameter from the parameter list. This constraint is mandatory to implement.

constrain

public void constrain(java.lang.String parametername,
                      double value,
                      double error)

Remembers a value and error bar to constrain the given parameter to. The actual constraining must happen in the Fitter, since it depends on the particular likelihood function in use.

constrain

public void constrain(java.lang.String parametername,
                      double value,
                      double pluserror,
                      double minuserror)

Remembers a value and error bars to constrain the given parameter to. The actual constraining must happen in the Fitter, since it depends on the particular likelihood function in use.