weka.classifiers.functions
Class GaussianProcessesAdaptive

java.lang.Object
  weka.classifiers.AbstractClassifier
      weka.classifiers.functions.GaussianProcessesAdaptive

All Implemented Interfaces:

Serializable, Cloneable, weka.classifiers.Classifier, weka.classifiers.IntervalEstimator, weka.core.CapabilitiesHandler, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler, weka.core.WeightedInstancesHandler

public class GaussianProcessesAdaptive
extends weka.classifiers.AbstractClassifier
implements weka.core.OptionHandler, weka.classifiers.IntervalEstimator, weka.core.TechnicalInformationHandler, weka.core.WeightedInstancesHandler

Implements Gaussian Processes for regression without hyperparameter-tuning. For more information see

David J.C. Mackay (1998). Introduction to Gaussian Processes. Dept. of Physics, Cambridge University, UK.

BibTeX:

 @misc{Mackay1998,
    address = {Dept. of Physics, Cambridge University, UK},
    author = {David J.C. Mackay},
    title = {Introduction to Gaussian Processes},
    year = {1998},
    PS = {http://wol.ra.phy.cam.ac.uk/mackay/gpB.ps.gz}
 }
 

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)

 -F <double>
  Level of fDev. (default 0.0)

 -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), 0 for full cache and
  -1 to turn it off.
  (default: 250007)

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

Version:

$Revision: 4521 $

Author:

Kurt Driessens (kurtd@cs.waikato.ac.nz)

See Also:

Serialized Form

Field Summary

static int	FILTER_NONE no filter
static int	FILTER_NORMALIZE normalizes the data
static int	FILTER_STANDARDIZE standardizes the data
protected double	m_Alin The parameters of the linear transforamtion realized by the filter on the class attribute
protected double	m_avg_target The training data.
protected double	m_Blin
protected boolean	m_checksTurnedOff Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a numeric class.
protected int	m_classIndex The class index from the training data
protected double	m_delta Gaussian Noise Value.
protected double	m_deltaClass Gaussian Noise Value for class.
protected weka.filters.Filter	m_Filter The filter used to standardize/normalize all values.
protected int	m_filterType Whether to normalize/standardize/neither
protected weka.classifiers.functions.supportVector.Kernel	m_kernel Kernel to use *
protected boolean	m_KernelIsLinear whether the kernel is a linear one
double[][]	m_L (negative) covariance matrix in symmetric matrix representation
protected weka.filters.unsupervised.attribute.ReplaceMissingValues	m_Missing The filter used to get rid of missing values.
protected weka.filters.unsupervised.attribute.NominalToBinary	m_NominalToBinary The filter used to make attributes numeric.
protected int	m_NumTrain The number of training instances
protected weka.core.matrix.Matrix	m_t The vector of target values.
static weka.core.Tag[]	TAGS_FILTER The filter to apply to the training data

Fields inherited from class weka.classifiers.AbstractClassifier

m_Debug

Constructor Summary

GaussianProcessesAdaptive()
the default constructor

Method Summary

void	buildClassifier(weka.core.Instances insts)
void	buildClassifier2(weka.core.Instances insts) Method for building the classifier.
double	classifyInstance(weka.core.Instance inst) Classifies a given instance.
String	classNoiseTipText() Returns the tip text for this property.
String	devTipText() Returns the tip text for this property.
String	filterTypeTipText() Returns the tip text for this property
weka.core.Capabilities	getCapabilities() Returns default capabilities of the classifier.
double	getClassNoise() Get the value of noise for the class.
double	getDev()
weka.core.SelectedTag	getFilterType() Gets how the training data will be transformed.
weka.classifiers.functions.supportVector.Kernel	getKernel() Gets the kernel to use.
double	getNoise() Get the value of noise.
String[]	getOptions() Gets the current settings of the classifier.
String	getRevision()
double	getStandardDeviation(weka.core.Instance inst) Gives the variance of the prediction at the given instance
weka.core.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.
static void	main(String[] argv) Main method for testing this class.
String	noiseTipText() Returns the tip text for this property
double[][]	predictIntervals(weka.core.Instance inst, double confidenceLevel) Predicts a confidence interval for the given instance and confidence level.
void	setClassNoise(double v) Set the level of Gaussian Noise for the class.
void	setDev(double v)
void	setFilterType(weka.core.SelectedTag newType) Sets how the training data will be transformed.
void	setKernel(weka.classifiers.functions.supportVector.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, makeCopies, makeCopy, runClassifier, setDebug

Methods inherited from class java.lang.Object

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

Field Detail

m_NominalToBinary

protected weka.filters.unsupervised.attribute.NominalToBinary m_NominalToBinary

The filter used to make attributes numeric.

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 weka.core.Tag[] TAGS_FILTER

The filter to apply to the training data

m_Filter

protected weka.filters.Filter m_Filter

The filter used to standardize/normalize all values.

m_filterType

protected int m_filterType

Whether to normalize/standardize/neither

m_Missing

protected weka.filters.unsupervised.attribute.ReplaceMissingValues m_Missing

The filter used to get rid of missing values.

m_checksTurnedOff

protected boolean m_checksTurnedOff

Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a numeric class.

m_delta

protected double m_delta

Gaussian Noise Value.

m_deltaClass

protected double m_deltaClass

Gaussian Noise Value for class.

m_classIndex

protected int m_classIndex

The class index from the training data

m_Alin

protected double m_Alin

The parameters of the linear transforamtion realized by the filter on the class attribute

m_Blin

protected double m_Blin

m_kernel

protected weka.classifiers.functions.supportVector.Kernel m_kernel

Kernel to use *

m_NumTrain

protected int m_NumTrain

The number of training instances

m_avg_target

protected double m_avg_target

The training data.

m_L

public double[][] m_L

(negative) covariance matrix in symmetric matrix representation

m_t

protected weka.core.matrix.Matrix m_t

The vector of target values.

m_KernelIsLinear

protected boolean m_KernelIsLinear

whether the kernel is a linear one

Constructor Detail

GaussianProcessesAdaptive

public GaussianProcessesAdaptive()

the default constructor

Method Detail

globalInfo

public String globalInfo()

Returns a string describing classifier

Returns:

a description suitable for displaying in the explorer/experimenter gui

getTechnicalInformation

public weka.core.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 weka.core.TechnicalInformationHandler

Returns:

the technical information about this class

getCapabilities

public weka.core.Capabilities getCapabilities()

Returns default capabilities of the classifier.

Specified by:

getCapabilities in interface weka.classifiers.Classifier

Specified by:

getCapabilities in interface weka.core.CapabilitiesHandler

Overrides:

getCapabilities in class weka.classifiers.AbstractClassifier

Returns:

the capabilities of this classifier

buildClassifier

public void buildClassifier(weka.core.Instances insts)
                     throws Exception

Specified by:

buildClassifier in interface weka.classifiers.Classifier

Throws:

Exception

buildClassifier2

public void buildClassifier2(weka.core.Instances insts)
                      throws Exception

Method for building the classifier.

Parameters:

insts - the set of training instances

Throws:

Exception - if the classifier can't be built successfully

classifyInstance

public double classifyInstance(weka.core.Instance inst)
                        throws Exception

Classifies a given instance.

Specified by:

classifyInstance in interface weka.classifiers.Classifier

Overrides:

classifyInstance in class weka.classifiers.AbstractClassifier

Parameters:

inst - the instance to be classified

Returns:

the classification

Throws:

Exception - if instance could not be classified successfully

predictIntervals

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

Predicts a confidence interval for the given instance and confidence level.

Specified by:

predictIntervals in interface weka.classifiers.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(weka.core.Instance inst)
                            throws Exception

Gives the variance of the prediction at the given instance

Parameters:

inst - the instance to get the variance for

Returns:

tha variance

Throws:

Exception - if computation fails

listOptions

public Enumeration listOptions()

Returns an enumeration describing the available options.

Specified by:

listOptions in interface weka.core.OptionHandler

Overrides:

listOptions in class weka.classifiers.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)

 -F <double>
  Level of fDev. (default 0.0)

 -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), 0 for full cache and
  -1 to turn it off.
  (default: 250007)

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

Specified by:

setOptions in interface weka.core.OptionHandler

Overrides:

setOptions in class weka.classifiers.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 weka.core.OptionHandler

Overrides:

getOptions in class weka.classifiers.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 weka.classifiers.functions.supportVector.Kernel getKernel()

Gets the kernel to use.

Returns:

the kernel

setKernel

public void setKernel(weka.classifiers.functions.supportVector.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 weka.core.SelectedTag getFilterType()

Gets how the training data will be transformed. Will be one of FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.2200Instances

Returns:

the filtering mode

setFilterType

public void setFilterType(weka.core.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.

devTipText

public String devTipText()

Returns the tip text for this property.

Returns:

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

setDev

public void setDev(double v)

getDev

public double getDev()

classNoiseTipText

public String classNoiseTipText()

Returns the tip text for this property.

Returns:

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

getClassNoise

public double getClassNoise()

Get the value of noise for the class.

Returns:

Value of noise.

setClassNoise

public void setClassNoise(double v)

Set the level of Gaussian Noise for the class.

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

getRevision

public String getRevision()

Specified by:

getRevision in interface weka.core.RevisionHandler

Overrides:

getRevision in class weka.classifiers.AbstractClassifier