java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.functions.GaussianProcessesWeighted

All Implemented Interfaces:: Serializable, Cloneable, weka.classifiers.Classifier, weka.classifiers.IntervalEstimator, weka.core.BatchPredictor, weka.core.CapabilitiesHandler, weka.core.CapabilitiesIgnorer, weka.core.CommandlineRunnable, weka.core.OptionHandler, weka.core.RevisionHandler, weka.core.TechnicalInformationHandler, weka.core.WeightedInstancesHandler

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

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: 1.0)

 -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:: $Revision$
Author:: Kurt Driessens ([email protected])
See Also:: Serialized Form

Field Summary

Fields
Modifier and Type	Field	Description
`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 weka.core.matrix.Matrix`	`m_C`	The covariance matrix.
`protected boolean`	`m_checksTurnedOff`	Turn off all checks and conversions?
`protected int`	`m_classIndex`	The class index from the training data
`protected double`	`m_delta`	Gaussian Noise Value.
`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
`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
BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor Description

GaussianProcessesWeighted()
the default constructor

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`void`	`buildClassifier(weka.core.Instances insts)`	Method for building the classifier.
`double`	`classifyInstance(weka.core.Instance inst)`	Classifies a given instance.
`String`	`filterTypeTipText()`	Returns the tip text for this property
`weka.core.Capabilities`	`getCapabilities()`	Returns default capabilities of the classifier.
`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()`	Returns the revision string.
`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`	`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
batchSizeTipText, debugTipText, distributionForInstance, distributionsForInstances, doNotCheckCapabilitiesTipText, forName, getBatchSize, getDebug, getDoNotCheckCapabilities, getNumDecimalPlaces, implementsMoreEfficientBatchPrediction, makeCopies, makeCopy, numDecimalPlacesTipText, postExecution, preExecution, run, runClassifier, setBatchSize, setDebug, setDoNotCheckCapabilities, setNumDecimalPlaces

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_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_C
```
protected weka.core.matrix.Matrix m_C
```
    The covariance matrix.
  - 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
  - GaussianProcessesWeighted
```
public GaussianProcessesWeighted()
```
    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.core.CapabilitiesHandler
    
    Specified by:
    
    getCapabilities in interface weka.classifiers.Classifier
    
    Overrides:
    
    getCapabilities in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the capabilities of this classifier
  - buildClassifier
```
public void buildClassifier(weka.core.Instances insts)
                     throws Exception
```
    Method for building the classifier.
    
    Specified by:
    
    buildClassifier in interface weka.classifiers.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: 1.0)
    
    -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.
  - toString
```
public String toString()
```
    Prints out the classifier.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    a description of the classifier as a string
  - getRevision
```
public String getRevision()
```
    Returns the revision string.
    
    Specified by:
    
    getRevision in interface weka.core.RevisionHandler
    
    Overrides:
    
    getRevision in class weka.classifiers.AbstractClassifier
    
    Returns:
    
    the revision
  - main
```
public static void main(String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - the commandline parameters

Class GaussianProcessesWeighted

Field Summary

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Field Detail

m_NominalToBinary

FILTER_NORMALIZE

FILTER_STANDARDIZE

FILTER_NONE

TAGS_FILTER

m_Filter

m_filterType

m_Missing

m_checksTurnedOff

m_delta

m_classIndex

m_Alin

m_Blin

m_kernel

m_NumTrain

m_avg_target

m_C

m_t

m_KernelIsLinear

Constructor Detail

GaussianProcessesWeighted

Method Detail

globalInfo

getTechnicalInformation

getCapabilities

buildClassifier

classifyInstance

predictIntervals

getStandardDeviation

listOptions

setOptions

getOptions

kernelTipText

getKernel

setKernel

filterTypeTipText

getFilterType

setFilterType

noiseTipText

getNoise

setNoise

toString

getRevision

main