weka.classifiers.functions
Class GaussianProcesses

java.lang.Object
  extended by weka.classifiers.AbstractClassifier
      extended by weka.classifiers.functions.GaussianProcesses
All Implemented Interfaces:
java.io.Serializable, java.lang.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: 5952 $
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.
 java.lang.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.
 java.lang.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.
 java.lang.String globalInfo()
          Returns a string describing classifier
 java.lang.String kernelTipText()
          Returns the tip text for this property
 java.util.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(java.lang.String[] argv)
          Main method for testing this class.
 java.lang.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(java.lang.String[] options)
          Parses a given list of options.
 java.lang.String toString()
          Prints out the classifier.
 
Methods inherited from class weka.classifiers.AbstractClassifier
debugTipText, distributionForInstance, forName, getDebug, getRevision, makeCopies, makeCopy, 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 java.lang.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 java.lang.Exception
Method for building the classifier.

Specified by:
buildClassifier in interface Classifier
Parameters:
insts - the set of training instances
Throws:
java.lang.Exception - if the classifier can't be built successfully

classifyInstance

public double classifyInstance(Instance inst)
                        throws java.lang.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:
java.lang.Exception - if instance could not be classified successfully

predictIntervals

public double[][] predictIntervals(Instance inst,
                                   double confidenceLevel)
                            throws java.lang.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:
java.lang.Exception - if interval could not be estimated successfully

getStandardDeviation

public double getStandardDeviation(Instance inst)
                            throws java.lang.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:
java.lang.Exception - if computation fails

logDensity

public double logDensity(Instance inst,
                         double value)
                  throws java.lang.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:
java.lang.Exception - if the density cannot be computed

listOptions

public java.util.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(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 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:
java.lang.Exception - if an option is not supported

getOptions

public java.lang.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 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

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 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

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 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

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 java.lang.String toString()
Prints out the classifier.

Overrides:
toString in class java.lang.Object
Returns:
a description of the classifier as a string

main

public static void main(java.lang.String[] argv)
Main method for testing this class.

Parameters:
argv - the commandline parameters