java.lang.Object
- weka.classifiers.Classifier
- - weka.classifiers.functions.GaussianProcesses

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

Field Summary

Fields
Modifier and Type	Field	Description
`static int`	`FILTER_NONE`	no filter
`static int`	`FILTER_NORMALIZE`	normalizes the data
`static int`	`FILTER_STANDARDIZE`	standardizes the data
`static Tag[]`	`TAGS_FILTER`	The filter to apply to the training data

Constructor Summary

Constructors
Constructor Description

GaussianProcesses()
the default constructor

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 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.
`java.lang.String`	`getRevision()`	Returns the revision string.
`double`	`getStandardDeviation(Instance inst)`	Gives the variance 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.
`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[][]`	`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

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
- Constructor Detail
  - GaussianProcesses
```
public GaussianProcesses()
```
    the default constructor
- 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 CapabilitiesHandler
    
    Overrides:
    
    getCapabilities in class Classifier
    
    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 class 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.
    
    Overrides:
    
    classifyInstance in class Classifier
    
    Parameters:
    
    inst - the instance to be classified
    
    Returns:
    
    the classification
    
    Throws:
    
    java.lang.Exception - if instance could not be classified successfully
  - 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 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 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
  - listOptions
```
public java.util.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(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 interface OptionHandler
    
    Overrides:
    
    setOptions in class Classifier
    
    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 Classifier
    
    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.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 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
  - getRevision
```
public java.lang.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(java.lang.String[] argv)
```
    Main method for testing this class.
    
    Parameters:
    
    argv - the commandline parameters

Class GaussianProcesses

Field Summary

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.Classifier

Methods inherited from class java.lang.Object

Field Detail

FILTER_NORMALIZE

FILTER_STANDARDIZE

FILTER_NONE

TAGS_FILTER

Constructor Detail

GaussianProcesses

Method Detail

globalInfo

getTechnicalInformation

getCapabilities

buildClassifier

classifyInstance

predictInterval

getStandardDeviation

listOptions

setOptions

getOptions

kernelTipText

getKernel

setKernel

filterTypeTipText

getFilterType

setFilterType

noiseTipText

getNoise

setNoise

toString

getRevision

main