weka.classifiers.functions

Class GaussianProcesses

java.lang.Object

weka.classifiers.AbstractClassifier

weka.classifiers.functions.GaussianProcesses

All Implemented Interfaces:

Serializable, Cloneable, Classifier, ConditionalDensityEstimator, IntervalEstimator, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class GaussianProcesses
extends AbstractClassifier
implements OptionHandler, IntervalEstimator, ConditionalDensityEstimator, TechnicalInformationHandler, WeightedInstancesHandler

Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. BibTeX:

Version:

$Revision: 8034 $

Author:

Kurt Driessens (kurtd@cs.waikato.ac.nz), Remco Bouckaert (remco@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

Fields

Modifier and Type	Field and Description
static int	FILTER_NONE no filter
static int	FILTER_NORMALIZE normalizes the data
static int	FILTER_STANDARDIZE standardizes the data
double[][]	m_L (negative) covariance matrix in symmetric matrix representation
static Tag[]	TAGS_FILTER The filter to apply to the training data

Constructor Summary

Constructors

Constructor and Description
GaussianProcesses()

Method Summary

Methods

Modifier and Type	Method and Description
void	buildClassifier(Instances insts) Method for building the classifier.
double	classifyInstance(Instance inst) Classifies a given instance.
String	filterTypeTipText() Returns the tip text for this property
Capabilities	getCapabilities() Returns default capabilities of the classifier.
SelectedTag	getFilterType() Gets how the training data will be transformed.
Kernel	getKernel() Gets the kernel to use.
double	getNoise() Get the value of noise.
String[]	getOptions() Gets the current settings of the classifier.
double	getStandardDeviation(Instance inst) Gives standard deviation of the prediction at the given instance.
TechnicalInformation	getTechnicalInformation() Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.
String	globalInfo() Returns a string describing classifier
String	kernelTipText() Returns the tip text for this property
Enumeration	listOptions() Returns an enumeration describing the available options.
double	logDensity(Instance inst, double value) Returns natural logarithm of density estimate for given value based on given instance.
static void	main(String[] argv) Main method for testing this class.
String	noiseTipText() Returns the tip text for this property
double[][]	predictIntervals(Instance inst, double confidenceLevel) Computes a prediction interval for the given instance and confidence level.
void	setFilterType(SelectedTag newType) Sets how the training data will be transformed.
void	setKernel(Kernel value) Sets the kernel to use.
void	setNoise(double v) Set the level of Gaussian Noise.
void	setOptions(String[] options) Parses a given list of options.
String	toString() Prints out the classifier.

Methods inherited from class weka.classifiers.AbstractClassifier

debugTipText, distributionForInstance, forName, getDebug, getRevision, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object

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

Field Detail

FILTER_NORMALIZE

public static final int FILTER_NORMALIZE

normalizes the data

See Also:

Constant Field Values

FILTER_STANDARDIZE

public static final int FILTER_STANDARDIZE

standardizes the data

See Also:

Constant Field Values

FILTER_NONE

public static final int FILTER_NONE

no filter

See Also:

Constant Field Values

TAGS_FILTER

public static final Tag[] TAGS_FILTER

The filter to apply to the training data

m_L

public double[][] m_L

(negative) covariance matrix in symmetric matrix representation

Constructor Detail

GaussianProcesses

public GaussianProcesses()

Method Detail

globalInfo

public String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

getTechnicalInformation

public TechnicalInformation getTechnicalInformation()

Returns an instance of a TechnicalInformation object, containing detailed information about the technical background of this class, e.g., paper reference or book this class is based on.

Specified by:

getTechnicalInformation in interface TechnicalInformationHandler

Returns:

the technical information about this class

getCapabilities

public Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface Classifier

Specified by:

getCapabilities in interface CapabilitiesHandler

Overrides:

getCapabilities in class AbstractClassifier

Returns:

the capabilities of this classifier

See Also:

Capabilities

buildClassifier

public void buildClassifier(Instances insts)
                     throws Exception

Method for building the classifier.

Specified by:

buildClassifier in interface Classifier

Parameters:

insts - the set of training instances

Throws:

Exception - if the classifier can't be built successfully

classifyInstance

public double classifyInstance(Instance inst)
                        throws Exception

Classifies a given instance.

Specified by:

classifyInstance in interface Classifier

Overrides:

classifyInstance in class AbstractClassifier

Parameters:

inst - the instance to be classified

Returns:

the classification

Throws:

Exception - if instance could not be classified successfully

predictIntervals

public double[][] predictIntervals(Instance inst,
                          double confidenceLevel)
                            throws Exception

Computes a prediction interval for the given instance and confidence level.

Specified by:

predictIntervals in interface IntervalEstimator

Parameters:

inst - the instance to make the prediction for

confidenceLevel - the percentage of cases the interval should cover

Returns:

a 1*2 array that contains the boundaries of the interval

Throws:

Exception - if interval could not be estimated successfully

getStandardDeviation

public double getStandardDeviation(Instance inst)
                            throws Exception

Gives standard deviation of the prediction at the given instance.

Parameters:

inst - the instance to get the standard deviation for

Returns:

the standard deviation

Throws:

Exception - if computation fails

logDensity

public double logDensity(Instance inst,
                double value)
                  throws Exception

Returns natural logarithm of density estimate for given value based on given instance.

Specified by:

logDensity in interface ConditionalDensityEstimator

Parameters:

instance - the instance to make the prediction for.

value - the value to make the prediction for.

Returns:

the natural logarithm of the density estimate

Throws:

Exception - if the density cannot be computed

listOptions

public Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface OptionHandler

Overrides:

listOptions in class AbstractClassifier

Returns:

an enumeration of all the available options.

setOptions

public void setOptions(String[] options)
                throws Exception

Parses a given list of options.

Valid options are:

       -D
        If set, classifier is run in debug mode and
        may output additional info to the console
 

       -L <double>
        Level of Gaussian Noise. (default 0.1)
 

       -M <double>
        Level of Gaussian Noise for the class. (default 0.1)
 

       -N
        Whether to 0=normalize/1=standardize/2=neither. (default 0=normalize)
 

       -K <classname and parameters>
        The Kernel to use.
        (default: weka.classifiers.functions.supportVector.PolyKernel)
 

       
       Options specific to kernel weka.classifiers.functions.supportVector.RBFKernel:
 

       -D
        Enables debugging output (if available) to be printed.
        (default: off)
 

       -no-checks
        Turns off all checks - use with caution!
        (default: checks on)
 

       -C <num>
        The size of the cache (a prime number).
        (default: 250007)
 

       -G <num>
        The Gamma parameter.
        (default: 0.01)
 

Specified by:

setOptions in interface OptionHandler

Overrides:

setOptions in class AbstractClassifier

Parameters:

options - the list of options as an array of strings

Throws:

Exception - if an option is not supported

getOptions

public String[] getOptions()

Gets the current settings of the classifier.

Specified by:

getOptions in interface OptionHandler

Overrides:

getOptions in class AbstractClassifier

Returns:

an array of strings suitable for passing to setOptions

kernelTipText

public String kernelTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getKernel

public Kernel getKernel()

Gets the kernel to use.

Returns:

the kernel

setKernel

public void setKernel(Kernel value)

Sets the kernel to use.

Parameters:

value - the new kernel

filterTypeTipText

public String filterTypeTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getFilterType

public SelectedTag getFilterType()

Gets how the training data will be transformed. Will be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.

Returns:

the filtering mode

setFilterType

public void setFilterType(SelectedTag newType)

Sets how the training data will be transformed. Should be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.

Parameters:

newType - the new filtering mode

noiseTipText

public String noiseTipText()

Returns the tip text for this property

Returns:

tip text for this property suitable for displaying in the explorer/experimenter gui

getNoise

public double getNoise()

Get the value of noise.

Returns:

Value of noise.

setNoise

public void setNoise(double v)

Set the level of Gaussian Noise.

Parameters:

v - Value to assign to noise.

toString

public String toString()

Prints out the classifier.

Overrides:

toString in class Object

Returns:

a description of the classifier as a string

main

public static void main(String[] argv)

Main method for testing this class.

Parameters:

argv - the commandline parameters