GaussianProcesses (interruptible-weka 0.1.4 API)

java.lang.Object
- weka.classifiers.AbstractClassifier
- - weka.classifiers.RandomizableClassifier
  - - weka.classifiers.functions.GaussianProcesses

All Implemented Interfaces:: java.io.Serializable, java.lang.Cloneable, Classifier, ConditionalDensityEstimator, IntervalEstimator, BatchPredictor, CapabilitiesHandler, CapabilitiesIgnorer, CommandlineRunnable, OptionHandler, Randomizable, RevisionHandler, TechnicalInformationHandler, WeightedInstancesHandler

public class GaussianProcesses
extends RandomizableClassifier
implements 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 as the other attributes (if normalization/standardizaton is turned on). Missing values are replaced by the global mean/mode. Nominal attributes are converted to binary ones. Note that kernel caching is turned off if the kernel used implements CachedKernel. *

* 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:

* *

  -L <double>
 *  Level of Gaussian Noise wrt transformed target. (default 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)

* *

  -S <num>
 *  Random number seed.
 *  (default 1)

* *

  -output-debug-info
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console

* *

  -do-not-check-capabilities
 *  If set, classifier capabilities are not checked before classifier is built
 *  (use with caution).

* *

  -num-decimal-places
 *  The number of decimal places for the output of numbers in the model (default 2).

* *


 * Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
 *

* *

  -E <num>
 *  The Exponent to use.
 *  (default: 1.0)

* *

  -L
 *  Use lower-order terms.
 *  (default: no)

* *

  -C <num>
 *  The size of the cache (a prime number), 0 for full cache and
 *  -1 to turn it off.
 *  (default: 250007)

* *

  -output-debug-info
 *  Enables debugging output (if available) to be printed.
 *  (default: off)

* *

  -no-checks
 *  Turns off all checks - use with caution!
 *  (default: checks on)

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
`protected Kernel`	`m_actualKernel` Actual kernel object to use
`protected double`	`m_Alin` The parameters of the linear transformation realized by the filter on the class attribute
`protected double`	`m_avg_target` The training data.
`protected double`	`m_Blin`
`protected boolean`	`m_checksTurnedOff` Turn off all checks and conversions?
`protected double`	`m_delta` Gaussian Noise Value.
`protected double`	`m_deltaSquared` The squared noise value.
`protected Filter`	`m_Filter` The filter used to standardize/normalize all values.
`protected int`	`m_filterType` Whether to normalize/standardize/neither
`protected Kernel`	`m_kernel` Template of kernel to use
`no.uib.cipr.matrix.Matrix`	`m_L` (negative) covariance matrix in symmetric matrix representation
`protected ReplaceMissingValues`	`m_Missing` The filter used to get rid of missing values.
`protected NominalToBinary`	`m_NominalToBinary` The filter used to make attributes numeric.
`protected int`	`m_NumTrain` The number of training instances
`protected no.uib.cipr.matrix.Vector`	`m_t` The vector of target values.
`protected double[]`	`m_weights` The weight of the training instances.
`static Tag[]`	`TAGS_FILTER` The filter to apply to the training data

Fields inherited from class weka.classifiers.RandomizableClassifier
m_Seed

Fields inherited from class weka.classifiers.AbstractClassifier
BATCH_SIZE_DEFAULT, m_BatchSize, m_Debug, m_DoNotCheckCapabilities, m_numDecimalPlaces, NUM_DECIMAL_PLACES_DEFAULT

Constructor Summary

Constructors
Constructor and Description

GaussianProcesses()

Constructors
Constructor and Description
`GaussianProcesses()`

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.
`protected double`	`computeStdDev(Instance inst, no.uib.cipr.matrix.Vector k)` Computes standard deviation for given instance, without transforming target back into original space.
`protected Instance`	`filterInstance(Instance inst)` Filters an 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<Option>`	`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.RandomizableClassifier
getSeed, seedTipText, setSeed

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

- Field Detail
  - m_NominalToBinary
```
protected NominalToBinary m_NominalToBinary
```
    The filter used to make attributes numeric.
  - 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_Filter
```
protected Filter m_Filter
```
    The filter used to standardize/normalize all values.
  - m_filterType
```
protected int m_filterType
```
    Whether to normalize/standardize/neither
  - m_Missing
```
protected ReplaceMissingValues m_Missing
```
    The filter used to get rid of missing values.
  - m_checksTurnedOff
```
protected boolean m_checksTurnedOff
```
    Turn off all checks and conversions? Turning them off assumes that data is purely numeric, doesn't contain any missing values, and has a numeric class.
  - m_delta
```
protected double m_delta
```
    Gaussian Noise Value.
  - m_deltaSquared
```
protected double m_deltaSquared
```
    The squared noise value.
  - m_Alin
```
protected double m_Alin
```
    The parameters of the linear transformation realized by the filter on the class attribute
  - m_Blin
```
protected double m_Blin
```
  - m_kernel
```
protected Kernel m_kernel
```
    Template of kernel to use
  - m_actualKernel
```
protected Kernel m_actualKernel
```
    Actual kernel object to use
  - m_NumTrain
```
protected int m_NumTrain
```
    The number of training instances
  - m_avg_target
```
protected double m_avg_target
```
    The training data.
  - m_L
```
public no.uib.cipr.matrix.Matrix m_L
```
    (negative) covariance matrix in symmetric matrix representation
  - m_t
```
protected no.uib.cipr.matrix.Vector m_t
```
    The vector of target values.
  - m_weights
```
protected double[] m_weights
```
    The weight of the training instances.
- 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
  - filterInstance
```
protected Instance filterInstance(Instance inst)
                           throws java.lang.Exception
```
    Filters an instance.
    
    Throws:
    
    java.lang.Exception
  - computeStdDev
```
protected double computeStdDev(Instance inst,
                               no.uib.cipr.matrix.Vector k)
                        throws java.lang.Exception
```
    Computes standard deviation for given instance, without transforming target back into original space.
    
    Throws:
    
    java.lang.Exception
  - 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:
    
    inst - 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<Option> listOptions()
```
    Returns an enumeration describing the available options.
    
    Specified by:
    
    listOptions in interface OptionHandler
    
    Overrides:
    
    listOptions in class RandomizableClassifier
    
    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:
    * *
```
  -L <double>
 *  Level of Gaussian Noise wrt transformed target. (default 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)
 
```
    * *
```
  -S <num>
 *  Random number seed.
 *  (default 1)
 
```
    * *
```
  -output-debug-info
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console
 
```
    * *
```
  -do-not-check-capabilities
 *  If set, classifier capabilities are not checked before classifier is built
 *  (use with caution).
 
```
    * *
```
  -num-decimal-places
 *  The number of decimal places for the output of numbers in the model (default 2).
 
```
    * *
```
 * Options specific to kernel weka.classifiers.functions.supportVector.PolyKernel:
 *
 
```
    * *
```
  -E <num>
 *  The Exponent to use.
 *  (default: 1.0)
 
```
    * *
```
  -L
 *  Use lower-order terms.
 *  (default: no)
 
```
    * *
```
  -C <num>
 *  The size of the cache (a prime number), 0 for full cache and
 *  -1 to turn it off.
 *  (default: 250007)
 
```
    * *
```
  -output-debug-info
 *  Enables debugging output (if available) to be printed.
 *  (default: off)
 
```
    * *
```
  -no-checks
 *  Turns off all checks - use with caution!
 *  (default: checks on)
 
```
    *
    Specified by:
    
    setOptions in interface OptionHandler
    
    Overrides:
    
    setOptions in class RandomizableClassifier
    
    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 RandomizableClassifier
    
    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

Class GaussianProcesses

Field Summary

Fields inherited from class weka.classifiers.RandomizableClassifier

Fields inherited from class weka.classifiers.AbstractClassifier

Constructor Summary

Method Summary

Methods inherited from class weka.classifiers.RandomizableClassifier

Methods inherited from class weka.classifiers.AbstractClassifier

Methods inherited from class java.lang.Object

Field Detail

m_NominalToBinary

FILTER_NORMALIZE

FILTER_STANDARDIZE

FILTER_NONE

TAGS_FILTER

m_Filter

m_filterType

m_Missing

m_checksTurnedOff

m_delta

m_deltaSquared

m_Alin

m_Blin

m_kernel

m_actualKernel

m_NumTrain

m_avg_target

m_L

m_t

m_weights

Constructor Detail

GaussianProcesses

Method Detail

globalInfo

getTechnicalInformation

getCapabilities

buildClassifier

classifyInstance

filterInstance

computeStdDev

predictIntervals

getStandardDeviation

logDensity

listOptions

setOptions

getOptions

kernelTipText

getKernel

setKernel

filterTypeTipText

getFilterType

setFilterType

noiseTipText

getNoise

setNoise

toString

main