weka.classifiers.functions
Class GaussianProcesses

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

All Implemented Interfaces:

Serializable, Cloneable, Classifier, ConditionalDensityEstimator, IntervalEstimator, CapabilitiesHandler, OptionHandler, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class GaussianProcesses
extends AbstractClassifier
implements OptionHandler, IntervalEstimator, ConditionalDensityEstimator, TechnicalInformationHandler, WeightedInstancesHandler

Implements Gaussian processes for regression without hyperparameter-tuning. To make choosing an appropriate noise level easier, this implementation applies normalization/standardization to the target attribute as well (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. BibTeX:

Version:
  
$Revision: 8034 $
Author:
  
Kurt Driessens (kurtd@cs.waikato.ac.nz), Remco Bouckaert (remco@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



 double[][]
m_L



          (negative) covariance matrix in symmetric matrix representation



static Tag[]
TAGS_FILTER



          The filter to apply to the training data


 



Constructor Summary
GaussianProcesses()



           
 



Method Summary




 void
buildClassifier(Instances insts)



          Method for building the classifier.



 double
classifyInstance(Instance inst)



          Classifies a given instance.



 String
filterTypeTipText()



          Returns the tip text for this property



 Capabilities
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.



 String[]
getOptions()



          Gets the current settings of the classifier.



 double
getStandardDeviation(Instance inst)



          Gives standard deviation of the prediction at the given instance.



 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.



 double
logDensity(Instance inst,
           double value)



          Returns natural logarithm of density estimate for given value based on given instance.



static void
main(String[] argv)



          Main method for testing this class.



 String
noiseTipText()



          Returns the tip text for this property



 double[][]
predictIntervals(Instance inst,
                 double confidenceLevel)



          Computes a prediction 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(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, getRevision, makeCopies, makeCopy, runClassifier, setDebug
 
Methods inherited from class java.lang.Object
equals, getClass, hashCode, notify, notifyAll, wait, wait, wait
 





Field Detail



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 Tag[] TAGS_FILTER

The filter to apply to the training data







m_L
public double[][] m_L

(negative) covariance matrix in symmetric matrix representation








Constructor Detail



GaussianProcesses
public GaussianProcesses()






Method Detail



globalInfo
public String globalInfo()

Returns a string describing classifier






Returns:
a description suitable for displaying in the
         explorer/experimenter gui





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 interface TechnicalInformationHandler



Returns:
the technical information about this class





getCapabilities
public Capabilities getCapabilities()

Returns default capabilities of the classifier.


Specified by:
getCapabilities in interface Classifier
Specified by:
getCapabilities in interface CapabilitiesHandler
Overrides:
getCapabilities in class AbstractClassifier



Returns:
the capabilities of this classifier
See Also:
Capabilities





buildClassifier
public void buildClassifier(Instances insts)
                     throws Exception

Method for building the classifier.


Specified by:
buildClassifier in interface Classifier


Parameters:
insts - the set of training instances
Throws:
Exception - if the classifier can't be built successfully





classifyInstance
public double classifyInstance(Instance inst)
                        throws Exception

Classifies a given instance.


Specified by:
classifyInstance in interface Classifier
Overrides:
classifyInstance in class AbstractClassifier


Parameters:
inst - the instance to be classified
Returns:
the classification
Throws:
Exception - if instance could not be classified successfully





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

Computes a prediction interval for the given instance and confidence
 level.


Specified by:
predictIntervals in interface 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(Instance inst)
                            throws Exception

Gives standard deviation of the prediction at the given instance.





Parameters:
inst - the instance to get the standard deviation for
Returns:
the standard deviation
Throws:
Exception - if computation fails





logDensity
public double logDensity(Instance inst,
                         double value)
                  throws Exception

Returns natural logarithm of density estimate for given value based on given instance.


Specified by:
logDensity in interface ConditionalDensityEstimator


Parameters:
instance - the instance to make the prediction for.
value - the value to make the prediction for.
Returns:
the natural logarithm of the density estimate
Throws:
Exception - if the density cannot be computed





listOptions
public Enumeration listOptions()

Returns an enumeration describing the available options.


Specified by:
listOptions in interface OptionHandler
Overrides:
listOptions in class 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)
 
 
 
       -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).
        (default: 250007)
 
 
 
       -G <num>
        The Gamma parameter.
        (default: 0.01)
 
 
 


Specified by:
setOptions in interface OptionHandler
Overrides:
setOptions in class 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 OptionHandler
Overrides:
getOptions in class 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 Kernel getKernel()

Gets the kernel to use.






Returns:
the kernel





setKernel
public void setKernel(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 SelectedTag getFilterType()

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






Returns:
the filtering mode





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





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





main
public static void main(String[] argv)

Main method for testing this class.





Parameters:
argv - the commandline parameters