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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.functions.GaussianProcesses
public class GaussianProcesses
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.
@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)
| Field Summary | |
|---|---|
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 | |
|---|---|
GaussianProcesses()
the default constructor |
|
| Method Summary | |
|---|---|
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 |
|---|
public static final int FILTER_NORMALIZE
public static final int FILTER_STANDARDIZE
public static final int FILTER_NONE
public static final Tag[] TAGS_FILTER
| Constructor Detail |
|---|
public GaussianProcesses()
| Method Detail |
|---|
public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation in interface TechnicalInformationHandlerpublic Capabilities getCapabilities()
getCapabilities in interface CapabilitiesHandlergetCapabilities in class ClassifierCapabilities
public void buildClassifier(Instances insts)
throws java.lang.Exception
buildClassifier in class Classifierinsts - the set of training instances
java.lang.Exception - if the classifier can't be built successfully
public double classifyInstance(Instance inst)
throws java.lang.Exception
classifyInstance in class Classifierinst - the instance to be classified
java.lang.Exception - if instance could not be classified
successfully
public double[][] predictInterval(Instance inst,
double confidenceLevel)
throws java.lang.Exception
predictInterval in interface IntervalEstimatorinst - the instance to make the prediction forconfidenceLevel - the percentage of cases the interval should cover
java.lang.Exception - if interval could not be estimated
successfully
public double getStandardDeviation(Instance inst)
throws java.lang.Exception
inst - the instance to get the variance for
java.lang.Exception - if computation failspublic java.util.Enumeration listOptions()
listOptions in interface OptionHandlerlistOptions in class Classifier
public void setOptions(java.lang.String[] options)
throws java.lang.Exception
-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)
setOptions in interface OptionHandlersetOptions in class Classifieroptions - the list of options as an array of strings
java.lang.Exception - if an option is not supportedpublic java.lang.String[] getOptions()
getOptions in interface OptionHandlergetOptions in class Classifierpublic java.lang.String kernelTipText()
public Kernel getKernel()
public void setKernel(Kernel value)
value - the new kernelpublic java.lang.String filterTypeTipText()
public SelectedTag getFilterType()
public void setFilterType(SelectedTag newType)
newType - the new filtering modepublic java.lang.String noiseTipText()
public double getNoise()
public void setNoise(double v)
v - Value to assign to noise.public java.lang.String toString()
toString in class java.lang.Objectpublic java.lang.String getRevision()
getRevision in interface RevisionHandlergetRevision in class Classifierpublic static void main(java.lang.String[] argv)
argv - the commandline parameters
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