Package weka.classifiers.functions
Class GaussianProcesses
- java.lang.Object
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- weka.classifiers.Classifier
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- weka.classifiers.functions.GaussianProcesses
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- All Implemented Interfaces:
java.io.Serializable
,java.lang.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:@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:
- 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 datastatic Tag[]
TAGS_FILTER
The filter to apply to the training data
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Constructor Summary
Constructors Constructor Description GaussianProcesses()
the default constructor
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Method Summary
All Methods Static Methods Instance Methods Concrete Methods Modifier and Type Method Description void
buildClassifier(Instances insts)
Method for building the classifier.double
classifyInstance(Instance inst)
Classifies a given instance.java.lang.String
filterTypeTipText()
Returns the tip text for this propertyCapabilities
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.java.lang.String[]
getOptions()
Gets the current settings of the classifier.java.lang.String
getRevision()
Returns the revision string.double
getStandardDeviation(Instance inst)
Gives the variance of the prediction at the given instanceTechnicalInformation
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.java.lang.String
globalInfo()
Returns a string describing classifierjava.lang.String
kernelTipText()
Returns the tip text for this propertyjava.util.Enumeration
listOptions()
Returns an enumeration describing the available options.static void
main(java.lang.String[] argv)
Main method for testing this class.java.lang.String
noiseTipText()
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
setKernel(Kernel value)
Sets the kernel to use.void
setNoise(double v)
Set the level of Gaussian Noise.void
setOptions(java.lang.String[] options)
Parses a given list of options.java.lang.String
toString()
Prints out the classifier.-
Methods inherited from class weka.classifiers.Classifier
debugTipText, distributionForInstance, forName, getDebug, makeCopies, makeCopy, setDebug
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Field Detail
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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 Tag[] TAGS_FILTER
The filter to apply to the training data
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Method Detail
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globalInfo
public java.lang.String globalInfo()
Returns a string describing classifier- Returns:
- a description suitable for displaying in the explorer/experimenter gui
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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 interfaceTechnicalInformationHandler
- Returns:
- the technical information about this class
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getCapabilities
public Capabilities getCapabilities()
Returns default capabilities of the classifier.- Specified by:
getCapabilities
in interfaceCapabilitiesHandler
- Overrides:
getCapabilities
in classClassifier
- Returns:
- the capabilities of this classifier
- See Also:
Capabilities
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buildClassifier
public void buildClassifier(Instances insts) throws java.lang.Exception
Method for building the classifier.- Specified by:
buildClassifier
in classClassifier
- Parameters:
insts
- the set of training instances- Throws:
java.lang.Exception
- if the classifier can't be built successfully
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classifyInstance
public double classifyInstance(Instance inst) throws java.lang.Exception
Classifies a given instance.- Overrides:
classifyInstance
in classClassifier
- Parameters:
inst
- the instance to be classified- Returns:
- the classification
- Throws:
java.lang.Exception
- if instance could not be classified successfully
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predictInterval
public double[][] predictInterval(Instance inst, double confidenceLevel) throws java.lang.Exception
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:
java.lang.Exception
- if interval could not be estimated successfully
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getStandardDeviation
public double getStandardDeviation(Instance inst) throws java.lang.Exception
Gives the variance of the prediction at the given instance- Parameters:
inst
- the instance to get the variance for- Returns:
- tha variance
- Throws:
java.lang.Exception
- if computation fails
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listOptions
public java.util.Enumeration 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
public void setOptions(java.lang.String[] options) throws java.lang.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 interfaceOptionHandler
- Overrides:
setOptions
in classClassifier
- Parameters:
options
- the list of options as an array of strings- Throws:
java.lang.Exception
- if an option is not supported
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getOptions
public java.lang.String[] 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
public java.lang.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 Kernel getKernel()
Gets the kernel to use.- Returns:
- the kernel
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setKernel
public void setKernel(Kernel value)
Sets the kernel to use.- Parameters:
value
- the new kernel
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filterTypeTipText
public java.lang.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 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(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 java.lang.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 java.lang.String toString()
Prints out the classifier.- Overrides:
toString
in classjava.lang.Object
- Returns:
- a description of the classifier as a string
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getRevision
public java.lang.String getRevision()
Returns the revision string.- Specified by:
getRevision
in interfaceRevisionHandler
- Overrides:
getRevision
in classClassifier
- Returns:
- the revision
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main
public static void main(java.lang.String[] argv)
Main method for testing this class.- Parameters:
argv
- the commandline parameters
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