Package weka.classifiers.functions
Class GaussianProcesses
java.lang.Object
weka.classifiers.Classifier
weka.classifiers.functions.GaussianProcesses
- All Implemented Interfaces:
Serializable
,Cloneable
,IntervalEstimator
,CapabilitiesHandler
,OptionHandler
,RevisionHandler
,TechnicalInformationHandler
public class GaussianProcesses
extends Classifier
implements OptionHandler, IntervalEstimator, 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:
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: 1.8 $
- Author:
- Kurt Driessens (kurtd@cs.waikato.ac.nz)
- See Also:
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Field Summary
FieldsModifier and TypeFieldDescriptionstatic final int
no filterstatic final int
normalizes the datastatic final int
standardizes the datastatic final Tag[]
The filter to apply to the training data -
Constructor Summary
Constructors -
Method Summary
Modifier and TypeMethodDescriptionvoid
buildClassifier
(Instances insts) Method for building the classifier.double
classifyInstance
(Instance inst) Classifies a given instance.Returns the tip text for this propertyReturns default capabilities of the classifier.Gets how the training data will be transformed.Gets the kernel to use.double
getNoise()
Get the value of noise.String[]
Gets the current settings of the classifier.Returns the revision string.double
getStandardDeviation
(Instance inst) Gives the variance of the prediction at the given instanceReturns 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.Returns a string describing classifierReturns the tip text for this propertyReturns an enumeration describing the available options.static void
Main method for testing this class.Returns the tip text for this propertydouble[][]
predictInterval
(Instance inst, double confidenceLevel) Predicts a confidence interval for the given instance and confidence level.void
setFilterType
(SelectedTag newType) Sets how the training data will be transformed.void
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.toString()
Prints out the classifier.Methods inherited from class weka.classifiers.Classifier
debugTipText, distributionForInstance, forName, getDebug, makeCopies, makeCopy, setDebug
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Field Details
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FILTER_NORMALIZE
public static final int FILTER_NORMALIZEnormalizes the data- See Also:
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FILTER_STANDARDIZE
public static final int FILTER_STANDARDIZEstandardizes the data- See Also:
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FILTER_NONE
public static final int FILTER_NONEno filter- See Also:
-
TAGS_FILTER
The filter to apply to the training data
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Constructor Details
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GaussianProcesses
public GaussianProcesses()the default constructor
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Method Details
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globalInfo
Returns a string describing classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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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 interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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getCapabilities
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classClassifier
- Returns:
- the capabilities of this classifier
- See Also:
-
buildClassifier
Method for building the classifier.- Specified by:
buildClassifier
in classClassifier
- Parameters:
insts
- the set of training instances- Throws:
Exception
- if the classifier can't be built successfully
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classifyInstance
Classifies a given instance.- Overrides:
classifyInstance
in classClassifier
- Parameters:
inst
- the instance to be classified- Returns:
- the classification
- Throws:
Exception
- if instance could not be classified successfully
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predictInterval
Predicts a confidence interval for the given instance and confidence level.- Specified by:
predictInterval
in interfaceIntervalEstimator
- 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
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
Returns an enumeration describing the available options.- Specified by:
listOptions
in interfaceOptionHandler
- Overrides:
listOptions
in classClassifier
- Returns:
- an enumeration of all the available options.
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setOptions
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 interfaceOptionHandler
- Overrides:
setOptions
in classClassifier
- Parameters:
options
- the list of options as an array of strings- Throws:
Exception
- if an option is not supported
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getOptions
Gets the current settings of the classifier.- Specified by:
getOptions
in interfaceOptionHandler
- Overrides:
getOptions
in classClassifier
- Returns:
- an array of strings suitable for passing to setOptions
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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
Gets the kernel to use.- Returns:
- the kernel
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setKernel
Sets the kernel to use.- Parameters:
value
- the new kernel
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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
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
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
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
Prints out the classifier. -
getRevision
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
Main method for testing this class.- Parameters:
argv
- the commandline parameters
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