GaussianProcesses

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 and 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 and Description

GaussianProcesses()
the default constructor

Constructors
Constructor and Description
`GaussianProcesses()` the default constructor

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method and 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