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java.lang.Objectweka.classifiers.Classifier
weka.classifiers.mi.MILR
public class MILR
Uses either standard or collective multi-instance assumption, but within linear regression. For the collective assumption, it offers arithmetic or geometric mean for the posteriors.
Valid options are:-D Turn on debugging output.
-R <ridge> Set the ridge in the log-likelihood.
-A [0|1|2] Defines the type of algorithm: 0. standard MI assumption 1. collective MI assumption, arithmetic mean for posteriors 2. collective MI assumption, geometric mean for posteriors
| Field Summary | |
|---|---|
static int |
ALGORITHMTYPE_ARITHMETIC
collective MI assumption, arithmetic mean for posteriors |
static int |
ALGORITHMTYPE_DEFAULT
standard MI assumption |
static int |
ALGORITHMTYPE_GEOMETRIC
collective MI assumption, geometric mean for posteriors |
static Tag[] |
TAGS_ALGORITHMTYPE
the types of algorithms |
| Constructor Summary | |
|---|---|
MILR()
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|
| Method Summary | |
|---|---|
java.lang.String |
algorithmTypeTipText()
Returns the tip text for this property |
void |
buildClassifier(Instances train)
Builds the classifier |
double[] |
distributionForInstance(Instance exmp)
Computes the distribution for a given exemplar |
SelectedTag |
getAlgorithmType()
Gets the type of algorithm. |
Capabilities |
getCapabilities()
Returns default capabilities of the classifier. |
Capabilities |
getMultiInstanceCapabilities()
Returns the capabilities of this multi-instance classifier for the relational data. |
java.lang.String[] |
getOptions()
Gets the current settings of the classifier. |
java.lang.String |
getRevision()
Returns the revision string. |
double |
getRidge()
Gets the ridge in the log-likelihood. |
java.lang.String |
globalInfo()
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 |
ridgeTipText()
Returns the tip text for this property |
void |
setAlgorithmType(SelectedTag newType)
Sets the algorithm type. |
void |
setOptions(java.lang.String[] options)
Parses a given list of options. |
void |
setRidge(double ridge)
Sets the ridge in the log-likelihood. |
java.lang.String |
toString()
Gets a string describing the classifier. |
| Methods inherited from class weka.classifiers.Classifier |
|---|
classifyInstance, debugTipText, 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 ALGORITHMTYPE_DEFAULT
public static final int ALGORITHMTYPE_ARITHMETIC
public static final int ALGORITHMTYPE_GEOMETRIC
public static final Tag[] TAGS_ALGORITHMTYPE
| Constructor Detail |
|---|
public MILR()
| Method Detail |
|---|
public java.lang.String globalInfo()
public java.util.Enumeration listOptions()
listOptions in interface OptionHandlerlistOptions in class Classifier
public void setOptions(java.lang.String[] options)
throws java.lang.Exception
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 ridgeTipText()
public void setRidge(double ridge)
ridge - the ridgepublic double getRidge()
public java.lang.String algorithmTypeTipText()
public SelectedTag getAlgorithmType()
public void setAlgorithmType(SelectedTag newType)
newType - the new algorithm typepublic Capabilities getCapabilities()
getCapabilities in interface CapabilitiesHandlergetCapabilities in class ClassifierCapabilitiespublic Capabilities getMultiInstanceCapabilities()
getMultiInstanceCapabilities in interface MultiInstanceCapabilitiesHandlerCapabilities
public void buildClassifier(Instances train)
throws java.lang.Exception
buildClassifier in class Classifiertrain - the training data to be used for generating the
boosted classifier.
java.lang.Exception - if the classifier could not be built successfully
public double[] distributionForInstance(Instance exmp)
throws java.lang.Exception
distributionForInstance in class Classifierexmp - the exemplar for which distribution is computed
java.lang.Exception - if the distribution can't be computed successfullypublic 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 - should contain the command line arguments to the
scheme (see Evaluation)
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