gov.sandia.cognition.learning.algorithm.regression

Class GaussNewtonAlgorithm

java.lang.Object

gov.sandia.cognition.util.AbstractCloneableSerializable

gov.sandia.cognition.algorithm.AbstractIterativeAlgorithm

gov.sandia.cognition.algorithm.AbstractAnytimeAlgorithm<ResultType>

gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,OutputType>>,ResultType>

gov.sandia.cognition.learning.algorithm.AbstractAnytimeSupervisedBatchLearner<Vector,Vector,ResultType>

gov.sandia.cognition.learning.algorithm.regression.AbstractParameterCostMinimizer<GradientDescendable,SumSquaredErrorCostFunction>

gov.sandia.cognition.learning.algorithm.regression.LeastSquaresEstimator

gov.sandia.cognition.learning.algorithm.regression.GaussNewtonAlgorithm

All Implemented Interfaces:

AnytimeAlgorithm<GradientDescendable>, IterativeAlgorithm, MeasurablePerformanceAlgorithm, StoppableAlgorithm, AnytimeBatchLearner<java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>>,GradientDescendable>, BatchCostMinimizationLearner<java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>>,GradientDescendable>, BatchLearner<java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>>,GradientDescendable>, ParameterCostMinimizer<GradientDescendable>, SupervisedBatchLearner<Vector,Vector,GradientDescendable>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

@PublicationReference(author="Wikipedia",
                      title="Gauss-Newton algorithm",
                      type=WebPage,
                      year=2009,
                      url="http://en.wikipedia.org/wiki/Gauss%E2%80%93Newton_algorithm")
public class GaussNewtonAlgorithm
extends LeastSquaresEstimator

Implementation of the Gauss-Newton parameter-estimation procedure. Please do not use this method, as it is extremely unstable, and really only for demonstration purposes only.

Instead, use the Fletcher-Xu hybrid method.

Since:

2.1

Author:

Kevin R. Dixon

See Also:

FletcherXuHybridEstimation, Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static LineMinimizer<?>	DEFAULT_LINE_MINIMIZER Default line minimizer, LineMinimizerDerivativeBased.
static double	STEP_MAX Maximum step norm allowed under a Gauss-Newton step, 100.0

Fields inherited from class gov.sandia.cognition.learning.algorithm.regression.AbstractParameterCostMinimizer

DEFAULT_MAX_ITERATIONS, DEFAULT_TOLERANCE

Fields inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

data, keepGoing

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

maxIterations

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

DEFAULT_ITERATION, iteration

Constructor Summary

Constructors

Constructor and Description
GaussNewtonAlgorithm() Creates a new instance of GaussNewtonAlgorithm
GaussNewtonAlgorithm(LineMinimizer<?> lineMinimizer) Creates a new instance of GaussNewtonAlgorithm
GaussNewtonAlgorithm(LineMinimizer<?> lineMinimizer, int maxIterations, double tolerance) Creates a new instance of GaussNewtonAlgorithm

Method Summary

Modifier and Type	Method and Description
protected void	cleanupAlgorithm() Called to clean up the learning algorithm's state after learning has finished.
LineMinimizer<?>	getLineMinimizer() Getter for lineMinimizer
protected boolean	initializeAlgorithm() Called to initialize the learning algorithm's state based on the data that is stored in the data field.
void	setLineMinimizer(LineMinimizer<?> lineMinimizer) Setter for lineMinimizer
protected boolean	step() Called to take a single step of the learning algorithm.

Methods inherited from class gov.sandia.cognition.learning.algorithm.regression.AbstractParameterCostMinimizer

getCostFunction, getObjectToOptimize, getPerformance, getResult, getResultCost, getTolerance, setCostFunction, setObjectToOptimize, setResult, setResultCost, setTolerance

Methods inherited from class gov.sandia.cognition.learning.algorithm.AbstractAnytimeBatchLearner

clone, getData, getKeepGoing, learn, setData, setKeepGoing, stop

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

getMaxIterations, isResultValid, setMaxIterations

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

addIterativeAlgorithmListener, fireAlgorithmEnded, fireAlgorithmStarted, fireStepEnded, fireStepStarted, getIteration, 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.learning.algorithm.BatchCostMinimizationLearner

learn

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

Field Detail

DEFAULT_LINE_MINIMIZER

public static final LineMinimizer<?> DEFAULT_LINE_MINIMIZER

Default line minimizer, LineMinimizerDerivativeBased.

STEP_MAX

public static final double STEP_MAX

Maximum step norm allowed under a Gauss-Newton step, 100.0

See Also:

Constant Field Values

Constructor Detail

GaussNewtonAlgorithm

public GaussNewtonAlgorithm()

Creates a new instance of GaussNewtonAlgorithm

GaussNewtonAlgorithm

public GaussNewtonAlgorithm(LineMinimizer<?> lineMinimizer)

Creates a new instance of GaussNewtonAlgorithm

Parameters:

lineMinimizer - Workhorse algorithm that finds the minimum along a particular direction

GaussNewtonAlgorithm

public GaussNewtonAlgorithm(LineMinimizer<?> lineMinimizer,
                            int maxIterations,
                            double tolerance)

Creates a new instance of GaussNewtonAlgorithm

Parameters:

lineMinimizer - Workhorse algorithm that finds the minimum along a particular direction

maxIterations - Maximum number of iterations

tolerance - Tolerance before stopping.

Method Detail

initializeAlgorithm

protected boolean initializeAlgorithm()

Description copied from class: AbstractAnytimeBatchLearner

Called to initialize the learning algorithm's state based on the data that is stored in the data field. The return value indicates if the algorithm can be run or not based on the initialization.

Specified by:

initializeAlgorithm in class AbstractAnytimeBatchLearner<java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>>,GradientDescendable>

Returns:

True if the learning algorithm can be run and false if it cannot.

step

protected boolean step()

Description copied from class: AbstractAnytimeBatchLearner

Called to take a single step of the learning algorithm.

Specified by:

step in class AbstractAnytimeBatchLearner<java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>>,GradientDescendable>

Returns:

True if another step can be taken and false it the algorithm should halt.

cleanupAlgorithm

protected void cleanupAlgorithm()

Description copied from class: AbstractAnytimeBatchLearner

Called to clean up the learning algorithm's state after learning has finished.

Specified by:

cleanupAlgorithm in class AbstractAnytimeBatchLearner<java.util.Collection<? extends InputOutputPair<? extends Vector,Vector>>,GradientDescendable>

getLineMinimizer

public LineMinimizer<?> getLineMinimizer()

Getter for lineMinimizer

Returns:

Workhorse algorithm that finds the minimum along a particular direction

setLineMinimizer

public void setLineMinimizer(LineMinimizer<?> lineMinimizer)

Setter for lineMinimizer

Parameters:

lineMinimizer - Workhorse algorithm that finds the minimum along a particular direction