gov.sandia.cognition.learning.algorithm.perceptron.kernel

Class KernelAdatron<InputType>

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<InputType,java.lang.Boolean,KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>>>

gov.sandia.cognition.learning.algorithm.perceptron.kernel.KernelAdatron<InputType>

Type Parameters:

InputType - Input type of the InputOutputPairs

All Implemented Interfaces:

AnytimeAlgorithm<KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>>>, IterativeAlgorithm, MeasurablePerformanceAlgorithm, StoppableAlgorithm, AnytimeBatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>>>, BatchLearner<java.util.Collection<? extends InputOutputPair<? extends InputType,java.lang.Boolean>>,KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>>>, SupervisedBatchLearner<InputType,java.lang.Boolean,KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>>>, CloneableSerializable, java.io.Serializable, java.lang.Cloneable

@CodeReview(reviewer="Kevin R. Dixon",
            date="2008-07-23",
            changesNeeded=false,
            comments={"Added PublicationReference to the original article.","Minor changes to javadoc.","Looks fine."})
 @PublicationReference(author={"Thilo-Thomas Friess","Nello Cristianini","Colin Campbell"},
                      title="The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines",
                      type=Conference,
                      publication="Proceedings of the Fifteenth International Conference on Machine Learning",
                      year=1998,
                      pages={188,196})
public class KernelAdatron<InputType>
extends AbstractAnytimeSupervisedBatchLearner<InputType,java.lang.Boolean,KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>>>
implements MeasurablePerformanceAlgorithm

The KernelAdatron class implements an online version of the Support Vector Machine learning algorithm. It is based on an extension of the Perceptron algorithm.

Since:

2.0

Author:

Justin Basilico

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static int	DEFAULT_MAX_ITERATIONS The default maximum number of iterations, 100.

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
KernelAdatron() Creates a new instance of KernelAdatron.
KernelAdatron(Kernel<? super InputType> kernel) Creates a new KernelAdatron with the given kernel.
KernelAdatron(Kernel<? super InputType> kernel, int maxIterations) Creates a new KernelAdatron with the given kernel and maximum number of iterations.

Method Summary

Modifier and Type	Method and Description
protected void	cleanupAlgorithm() Called to clean up the learning algorithm's state after learning has finished.
int	getErrorCount() Gets the error count of the most recent iteration.
Kernel<? super InputType>	getKernel() Gets the kernel to use.
NamedValue<java.lang.Integer>	getPerformance() Gets the name-value pair that describes the current performance of the algorithm.
KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>>	getResult() Gets the current result of the algorithm.
protected java.util.LinkedHashMap<InputOutputPair<? extends InputType,java.lang.Boolean>,DefaultWeightedValue<InputType>>	getSupportsMap() Gets the mapping of examples to weight objects (support vectors).
protected boolean	initializeAlgorithm() Called to initialize the learning algorithm's state based on the data that is stored in the data field.
protected void	setErrorCount(int errorCount) Sets the error count of the most recent iteration.
void	setKernel(Kernel<? super InputType> kernel) Sets the kernel to use.
protected void	setLearned(KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>> result) Sets the object currently being result.
protected void	setSupportsMap(java.util.LinkedHashMap<InputOutputPair<? extends InputType,java.lang.Boolean>,DefaultWeightedValue<InputType>> supportsMap) Gets the mapping of examples to weight objects (support vectors).
protected boolean	step() Called to take a single step of the learning algorithm.

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.BatchLearner

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

Field Detail

DEFAULT_MAX_ITERATIONS

public static final int DEFAULT_MAX_ITERATIONS

The default maximum number of iterations, 100.

See Also:

Constant Field Values

Constructor Detail

KernelAdatron

public KernelAdatron()

Creates a new instance of KernelAdatron.

KernelAdatron

public KernelAdatron(Kernel<? super InputType> kernel)

Creates a new KernelAdatron with the given kernel.

Parameters:

kernel - The kernel to use.

KernelAdatron

public KernelAdatron(Kernel<? super InputType> kernel,
                     int maxIterations)

Creates a new KernelAdatron with the given kernel and maximum number of iterations.

Parameters:

kernel - The kernel to use.

maxIterations - The maximum number of iterations.

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 InputType,java.lang.Boolean>>,KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>>>

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 InputType,java.lang.Boolean>>,KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>>>

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 InputType,java.lang.Boolean>>,KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>>>

getKernel

public Kernel<? super InputType> getKernel()

Gets the kernel to use.

Returns:

The kernel to use.

setKernel

public void setKernel(Kernel<? super InputType> kernel)

Sets the kernel to use.

Parameters:

kernel - The kernel to use.

getResult

public KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>> getResult()

Description copied from interface: AnytimeAlgorithm

Gets the current result of the algorithm.

Specified by:

getResult in interface AnytimeAlgorithm<KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>>>

Returns:

Current result of the algorithm.

setLearned

protected void setLearned(KernelBinaryCategorizer<InputType,DefaultWeightedValue<InputType>> result)

Sets the object currently being result.

Parameters:

result - The object currently being result.

getErrorCount

public int getErrorCount()

Gets the error count of the most recent iteration.

Returns:

The current error count.

setErrorCount

protected void setErrorCount(int errorCount)

Sets the error count of the most recent iteration.

Parameters:

errorCount - The current error count.

getSupportsMap

protected java.util.LinkedHashMap<InputOutputPair<? extends InputType,java.lang.Boolean>,DefaultWeightedValue<InputType>> getSupportsMap()

Gets the mapping of examples to weight objects (support vectors).

Returns:

The mapping of examples to weight objects.

setSupportsMap

protected void setSupportsMap(java.util.LinkedHashMap<InputOutputPair<? extends InputType,java.lang.Boolean>,DefaultWeightedValue<InputType>> supportsMap)

Gets the mapping of examples to weight objects (support vectors).

Parameters:

supportsMap - The mapping of examples to weight objects.

getPerformance

public NamedValue<java.lang.Integer> getPerformance()

Description copied from interface: MeasurablePerformanceAlgorithm

Gets the name-value pair that describes the current performance of the algorithm. For most algorithms, this is the value that they are attempting to optimize.

Specified by:

getPerformance in interface MeasurablePerformanceAlgorithm

Returns:

The name-value pair that describes the current performance of the algorithm.