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:

 @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:
  • Field Details

    • FILTER_NORMALIZE

      public static final int FILTER_NORMALIZE
      normalizes the data
      See Also:
    • FILTER_STANDARDIZE

      public static final int FILTER_STANDARDIZE
      standardizes the data
      See Also:
    • FILTER_NONE

      public static final int FILTER_NONE
      no filter
      See Also:
    • TAGS_FILTER

      public static final Tag[] TAGS_FILTER
      The filter to apply to the training data
  • Constructor Details

    • GaussianProcesses

      public GaussianProcesses()
      the default constructor
  • Method Details

    • 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 CapabilitiesHandler
      Overrides:
      getCapabilities in class Classifier
      Returns:
      the capabilities of this classifier
      See Also:
    • buildClassifier

      public void buildClassifier(Instances insts) throws Exception
      Method for building the classifier.
      Specified by:
      buildClassifier in class 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.
      Overrides:
      classifyInstance in class Classifier
      Parameters:
      inst - the instance to be classified
      Returns:
      the classification
      Throws:
      Exception - if instance could not be classified successfully
    • predictInterval

      public double[][] predictInterval(Instance inst, double confidenceLevel) throws Exception
      Predicts a confidence interval for the given instance and confidence level.
      Specified by:
      predictInterval 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 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
    • listOptions

      public Enumeration listOptions()
      Returns an enumeration describing the available options.
      Specified by:
      listOptions in interface OptionHandler
      Overrides:
      listOptions in class Classifier
      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: 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 interface OptionHandler
      Overrides:
      setOptions in class Classifier
      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 Classifier
      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.2200Instances
      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
    • getRevision

      public String getRevision()
      Returns the revision string.
      Specified by:
      getRevision in interface RevisionHandler
      Overrides:
      getRevision in class Classifier
      Returns:
      the revision
    • main

      public static void main(String[] argv)
      Main method for testing this class.
      Parameters:
      argv - the commandline parameters