Class GaussianProcesses

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:

  • Capabilities

• 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