Package weka.classifiers.functions
Class GaussianProcessesWeighted
- java.lang.Object
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- weka.classifiers.AbstractClassifier
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- weka.classifiers.functions.GaussianProcessesWeighted
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- 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
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Field Summary
Fields Modifier and Type Field Description static int
FILTER_NONE
no filterstatic int
FILTER_NORMALIZE
normalizes the datastatic int
FILTER_STANDARDIZE
standardizes the dataprotected double
m_Alin
The parameters of the linear transforamtion realized by the filter on the class attributeprotected 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? 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 dataprotected 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/neitherprotected weka.classifiers.functions.supportVector.Kernel
m_kernel
Kernel to useprotected boolean
m_KernelIsLinear
whether the kernel is a linear oneprotected 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 instancesprotected 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
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Constructor Summary
Constructors Constructor Description GaussianProcessesWeighted()
the default constructor
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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 propertyweka.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 instanceweka.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 classifierString
kernelTipText()
Returns the tip text for this propertyEnumeration
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 propertydouble[][]
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
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Field Detail
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m_NominalToBinary
protected weka.filters.unsupervised.attribute.NominalToBinary m_NominalToBinary
The filter used to make attributes numeric.
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FILTER_NORMALIZE
public static final int FILTER_NORMALIZE
normalizes the data- See Also:
- Constant Field Values
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FILTER_STANDARDIZE
public static final int FILTER_STANDARDIZE
standardizes the data- See Also:
- Constant Field Values
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FILTER_NONE
public static final int FILTER_NONE
no filter- See Also:
- Constant Field Values
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TAGS_FILTER
public static final weka.core.Tag[] TAGS_FILTER
The filter to apply to the training data
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m_Filter
protected weka.filters.Filter m_Filter
The filter used to standardize/normalize all values.
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m_filterType
protected int m_filterType
Whether to normalize/standardize/neither
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m_Missing
protected weka.filters.unsupervised.attribute.ReplaceMissingValues m_Missing
The filter used to get rid of missing values.
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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.
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m_delta
protected double m_delta
Gaussian Noise Value.
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m_classIndex
protected int m_classIndex
The class index from the training data
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m_Alin
protected double m_Alin
The parameters of the linear transforamtion realized by the filter on the class attribute
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m_Blin
protected double m_Blin
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m_kernel
protected weka.classifiers.functions.supportVector.Kernel m_kernel
Kernel to use
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m_NumTrain
protected int m_NumTrain
The number of training instances
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m_avg_target
protected double m_avg_target
The training data.
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m_C
protected weka.core.matrix.Matrix m_C
The covariance matrix.
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m_t
protected weka.core.matrix.Matrix m_t
The vector of target values.
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m_KernelIsLinear
protected boolean m_KernelIsLinear
whether the kernel is a linear one
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Method Detail
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globalInfo
public String globalInfo()
Returns a string describing classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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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 interfaceweka.core.TechnicalInformationHandler
- Returns:
- the technical information about this class
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getCapabilities
public weka.core.Capabilities getCapabilities()
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceweka.core.CapabilitiesHandler
- Specified by:
getCapabilities
in interfaceweka.classifiers.Classifier
- Overrides:
getCapabilities
in classweka.classifiers.AbstractClassifier
- Returns:
- the capabilities of this classifier
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buildClassifier
public void buildClassifier(weka.core.Instances insts) throws Exception
Method for building the classifier.- Specified by:
buildClassifier
in interfaceweka.classifiers.Classifier
- Parameters:
insts
- the set of training instances- Throws:
Exception
- if the classifier can't be built successfully
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classifyInstance
public double classifyInstance(weka.core.Instance inst) throws Exception
Classifies a given instance.- Specified by:
classifyInstance
in interfaceweka.classifiers.Classifier
- Overrides:
classifyInstance
in classweka.classifiers.AbstractClassifier
- Parameters:
inst
- the instance to be classified- Returns:
- the classification
- Throws:
Exception
- if instance could not be classified successfully
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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 interfaceweka.classifiers.IntervalEstimator
- Parameters:
inst
- the instance to make the prediction forconfidenceLevel
- 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
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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
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listOptions
public Enumeration listOptions()
Returns an enumeration describing the available options.- Specified by:
listOptions
in interfaceweka.core.OptionHandler
- Overrides:
listOptions
in classweka.classifiers.AbstractClassifier
- Returns:
- an enumeration of all the available options.
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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 interfaceweka.core.OptionHandler
- Overrides:
setOptions
in classweka.classifiers.AbstractClassifier
- Parameters:
options
- the list of options as an array of strings- Throws:
Exception
- if an option is not supported
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getOptions
public String[] getOptions()
Gets the current settings of the classifier.- Specified by:
getOptions
in interfaceweka.core.OptionHandler
- Overrides:
getOptions
in classweka.classifiers.AbstractClassifier
- Returns:
- an array of strings suitable for passing to setOptions
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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
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getKernel
public weka.classifiers.functions.supportVector.Kernel getKernel()
Gets the kernel to use.- Returns:
- the kernel
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setKernel
public void setKernel(weka.classifiers.functions.supportVector.Kernel value)
Sets the kernel to use.- Parameters:
value
- the new kernel
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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
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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
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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
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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
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getNoise
public double getNoise()
Get the value of noise.- Returns:
- Value of noise.
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setNoise
public void setNoise(double v)
Set the level of Gaussian Noise.- Parameters:
v
- Value to assign to noise.
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toString
public String toString()
Prints out the classifier.
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getRevision
public String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceweka.core.RevisionHandler
- Overrides:
getRevision
in classweka.classifiers.AbstractClassifier
- Returns:
- the revision
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main
public static void main(String[] argv)
Main method for testing this class.- Parameters:
argv
- the commandline parameters
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