gov.sandia.cognition.learning.algorithm.root

Class MinimizerBasedRootFinder

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

gov.sandia.cognition.algorithm.AnytimeAlgorithmWrapper<InputOutputPair<java.lang.Double,java.lang.Double>,LineMinimizer<Evaluator<java.lang.Double,java.lang.Double>>>

gov.sandia.cognition.learning.algorithm.root.MinimizerBasedRootFinder

All Implemented Interfaces:

AnytimeAlgorithm<InputOutputPair<java.lang.Double,java.lang.Double>>, IterativeAlgorithm, IterativeAlgorithmListener, StoppableAlgorithm, BatchLearner<Evaluator<java.lang.Double,java.lang.Double>,InputOutputPair<java.lang.Double,java.lang.Double>>, RootFinder, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

public class MinimizerBasedRootFinder
extends AnytimeAlgorithmWrapper<InputOutputPair<java.lang.Double,java.lang.Double>,LineMinimizer<Evaluator<java.lang.Double,java.lang.Double>>>
implements RootFinder

A root finder that uses minimization techniques to find the roots (zero-crossings).

Since:

3.0

Author:

Kevin R. Dixon

See Also:

Serialized Form

Field Summary

Fields inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

DEFAULT_ITERATION, iteration

Constructor Summary

Constructors

Constructor and Description
MinimizerBasedRootFinder() Creates a new instance of MinimizerBasedRootFinder
MinimizerBasedRootFinder(LineMinimizer<Evaluator<java.lang.Double,java.lang.Double>> algorithm) Creates a new instance of MinimizerBasedRootFinder

Method Summary

Modifier and Type	Method and Description
double	getInitialGuess() Sets the initial guess of the root (zero-crossing), which is supplied as input to the function to find the zero-crossings of.
InputOutputPair<java.lang.Double,java.lang.Double>	getResult() Gets the current result of the algorithm.
double	getTolerance() Gets the tolerance of the algorithm.
InputOutputPair<java.lang.Double,java.lang.Double>	learn(Evaluator<java.lang.Double,java.lang.Double> data) The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.
void	setInitialGuess(double intitialGuess) Sets the initial guess of the root (zero-crossing), which is supplied as input to the function to find the zero-crossings of.
void	setTolerance(double tolerance) Sets the tolerance of the algorithm.

Methods inherited from class gov.sandia.cognition.algorithm.AnytimeAlgorithmWrapper

algorithmEnded, algorithmStarted, clone, getAlgorithm, getIteration, getMaxIterations, isResultValid, readResolve, setAlgorithm, setMaxIterations, stepEnded, stepStarted, stop

Methods inherited from class gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

addIterativeAlgorithmListener, fireAlgorithmEnded, fireAlgorithmStarted, fireStepEnded, fireStepStarted, getListeners, removeIterativeAlgorithmListener, setIteration, setListeners

Methods inherited from class java.lang.Object

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

Methods inherited from interface gov.sandia.cognition.util.CloneableSerializable

clone

Methods inherited from interface gov.sandia.cognition.algorithm.AnytimeAlgorithm

getMaxIterations, setMaxIterations

Methods inherited from interface gov.sandia.cognition.algorithm.IterativeAlgorithm

addIterativeAlgorithmListener, getIteration, removeIterativeAlgorithmListener

Methods inherited from interface gov.sandia.cognition.algorithm.StoppableAlgorithm

isResultValid, stop

Constructor Detail

MinimizerBasedRootFinder

public MinimizerBasedRootFinder()

Creates a new instance of MinimizerBasedRootFinder

MinimizerBasedRootFinder

public MinimizerBasedRootFinder(LineMinimizer<Evaluator<java.lang.Double,java.lang.Double>> algorithm)

Creates a new instance of MinimizerBasedRootFinder

Parameters:

algorithm - Minimization algorithm.

Method Detail

getResult

public InputOutputPair<java.lang.Double,java.lang.Double> getResult()

Description copied from interface: AnytimeAlgorithm

Gets the current result of the algorithm.

Specified by:

getResult in interface AnytimeAlgorithm<InputOutputPair<java.lang.Double,java.lang.Double>>

Returns:

Current result of the algorithm.

getInitialGuess

public double getInitialGuess()

Description copied from interface: RootFinder

Sets the initial guess of the root (zero-crossing), which is supplied as input to the function to find the zero-crossings of.

Specified by:

getInitialGuess in interface RootFinder

Returns:

Initial guess of the root location.

setInitialGuess

public void setInitialGuess(double intitialGuess)

Description copied from interface: RootFinder

Sets the initial guess of the root (zero-crossing), which is supplied as input to the function to find the zero-crossings of.

Specified by:

setInitialGuess in interface RootFinder

Parameters:

intitialGuess - Initial guess of the root location.

setTolerance

public void setTolerance(double tolerance)

Description copied from interface: RootFinder

Sets the tolerance of the algorithm. Smaller tolerances may yield more accurate estimates but spend more computation finding them.

Specified by:

setTolerance in interface RootFinder

Parameters:

tolerance - Tolerance, where tolerances closer to zero are more accurate, and larger tolerances are less accurate. In any case, tolerance must be greater than or equal to zero.

getTolerance

public double getTolerance()

Description copied from interface: RootFinder

Gets the tolerance of the algorithm.

Specified by:

getTolerance in interface RootFinder

Returns:

Tolerance, where tolerances closer to zero are more accurate, and larger tolerances are less accurate. In any case, tolerance must be greater than or equal to zero.

learn

public InputOutputPair<java.lang.Double,java.lang.Double> learn(Evaluator<java.lang.Double,java.lang.Double> data)

Description copied from interface: BatchLearner

The learn method creates an object of ResultType using data of type DataType, using some form of "learning" algorithm.

Specified by:

learn in interface BatchLearner<Evaluator<java.lang.Double,java.lang.Double>,InputOutputPair<java.lang.Double,java.lang.Double>>

Parameters:

data - The data that the learning algorithm will use to create an object of ResultType.

Returns:

The object that is created based on the given data using the learning algorithm.