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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.RandomizableClassifier
weka.classifiers.mi.TLD
public class TLD
Two-Level Distribution approach, changes the starting value of the searching algorithm, supplement the cut-off modification and check missing values.
For more information see:
Xin Xu (2003). Statistical learning in multiple instance problem. Hamilton, NZ.
@mastersthesis{Xu2003,
address = {Hamilton, NZ},
author = {Xin Xu},
note = {0657.594},
school = {University of Waikato},
title = {Statistical learning in multiple instance problem},
year = {2003}
}
Valid options are:
-C Set whether or not use empirical log-odds cut-off instead of 0
-R <numOfRuns> Set the number of multiple runs needed for searching the MLE.
-S <num> Random number seed. (default 1)
-D If set, classifier is run in debug mode and may output additional info to the console
| Field Summary | |
|---|---|
static double |
ZERO
The very small number representing zero |
| Constructor Summary | |
|---|---|
TLD()
|
|
| Method Summary | |
|---|---|
void |
buildClassifier(Instances exs)
Generates a classifier. |
double |
classifyInstance(Instance ex)
Classifies the given test instance. |
Capabilities |
getCapabilities()
Returns default capabilities of the classifier. |
Capabilities |
getMultiInstanceCapabilities()
Returns the capabilities of this multi-instance classifier for the relational data. |
int |
getNumRuns()
Returns the number of runs to perform. |
java.lang.String[] |
getOptions()
Gets the current settings of the Classifier. |
java.lang.String |
getRevision()
Returns the revision string. |
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. |
boolean |
getUsingCutOff()
Returns whether an empirical cutoff is used |
java.lang.String |
globalInfo()
Returns a string describing this filter |
java.util.Enumeration |
listOptions()
Returns an enumeration describing the available options |
static void |
main(java.lang.String[] args)
Main method for testing. |
java.lang.String |
numRunsTipText()
Returns the tip text for this property |
void |
setNumRuns(int numRuns)
Sets the number of runs to perform. |
void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
void |
setUsingCutOff(boolean cutOff)
Sets whether to use an empirical cutoff. |
java.lang.String |
usingCutOffTipText()
Returns the tip text for this property |
| Methods inherited from class weka.classifiers.RandomizableClassifier |
|---|
getSeed, seedTipText, setSeed |
| 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, toString, wait, wait, wait |
| Field Detail |
|---|
public static double ZERO
| Constructor Detail |
|---|
public TLD()
| Method Detail |
|---|
public java.lang.String globalInfo()
public TechnicalInformation getTechnicalInformation()
getTechnicalInformation in interface TechnicalInformationHandlerpublic Capabilities getCapabilities()
getCapabilities in interface CapabilitiesHandlergetCapabilities in class ClassifierCapabilitiespublic Capabilities getMultiInstanceCapabilities()
getMultiInstanceCapabilities in interface MultiInstanceCapabilitiesHandlerCapabilities
public void buildClassifier(Instances exs)
throws java.lang.Exception
Classifier
buildClassifier in class Classifierexs - the training exemplars
java.lang.Exception - if the model cannot be built properly
public double classifyInstance(Instance ex)
throws java.lang.Exception
Classifier
classifyInstance in class Classifierex - the given test exemplar
java.lang.Exception - if the exemplar could not be classified
successfullypublic java.util.Enumeration listOptions()
listOptions in interface OptionHandlerlistOptions in class RandomizableClassifier
public void setOptions(java.lang.String[] options)
throws java.lang.Exception
-C Set whether or not use empirical log-odds cut-off instead of 0
-R <numOfRuns> Set the number of multiple runs needed for searching the MLE.
-S <num> Random number seed. (default 1)
-D If set, classifier is run in debug mode and may output additional info to the console
setOptions in interface OptionHandlersetOptions in class RandomizableClassifieroptions - 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 RandomizableClassifierpublic java.lang.String numRunsTipText()
public void setNumRuns(int numRuns)
numRuns - the number of runs to performpublic int getNumRuns()
public java.lang.String usingCutOffTipText()
public void setUsingCutOff(boolean cutOff)
cutOff - whether to use an empirical cutoffpublic boolean getUsingCutOff()
public java.lang.String getRevision()
getRevision in interface RevisionHandlergetRevision in class Classifierpublic static void main(java.lang.String[] args)
args - the options for the classifier
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