public class VCModLEM
extends weka.classifiers.Classifier
Modifier and Type | Field | Description |
---|---|---|
static int |
iFold |
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static weka.core.Tag[] |
TAGS_CLASSIFICATION_STRATEGY |
The classification strategy modes
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static weka.core.Tag[] |
TAGS_POST_PRUNING_TYPE |
The post-pruning type modes
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static weka.core.Tag[] |
TAGS_RULE_QUALITY |
The classification strategy modes
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static weka.core.Tag[] |
TAGS_RULES_TYPE |
The rules type modes
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static weka.core.Tag[] |
TAGS_SELECTION_CRITERION |
The selection criterion modes
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static weka.core.Tag[] |
TAGS_VCMEASURE_TYPE |
The rules type modes
|
Constructor | Description |
---|---|
VCModLEM() |
Modifier and Type | Method | Description |
---|---|---|
boolean |
areTwoInstancesEqual(int firstInstance,
int secondInstance) |
Checks if two given instances are equal
|
void |
buildClassifier(weka.core.Instances dataset) |
|
java.lang.String |
classificationStrategyTipText() |
|
double |
classifyInstance(weka.core.Instance instance) |
Classifies the given test instance.
|
double[] |
distributionForInstance(weka.core.Instance instance) |
Predicts the class memberships for a given instance.
|
double |
Eval(java.util.BitSet subSet1,
java.util.BitSet subSet2) |
|
double |
EvalEntropy(java.util.BitSet subSet1,
java.util.BitSet subSet2) |
|
double |
EvalLaplace(java.util.BitSet subSet1,
java.util.BitSet subSet2) |
|
VCMLSelector |
findBestNominalSelector(int iSelectors) |
|
VCMLSelector |
findBestNumericalSelector(int iSelectors) |
|
VCMLSelector |
findBestSelector() |
|
java.lang.String |
forwardPrunningCoefficientTipText() |
Returns the tip text for this property
|
void |
generateApproximation() |
|
void |
generateNominalSelectors(int attrNum) |
|
void |
generateNumericalSelectors(int attrNum) |
|
void |
generateSelectors() |
|
double |
getBestEval() |
gets the best possible evaluation for given criterion of selection
|
weka.core.Capabilities |
getCapabilities() |
Returns default capabilities of the classifier tree.
|
weka.core.SelectedTag |
getClassificationStrategy() |
Gets the classification strategy used.
|
double |
getForwardPrunningCoefficient() |
Get the value of forwardPrunningCoefficient.
|
java.lang.String[] |
getOptions() |
Gets the current settings of the classifier.
|
weka.core.SelectedTag |
getPostPruningType() |
Gets the post-pruning type used.
|
double |
getPostPrunningCoefficient() |
Get the value of forwardPrunningCoefficient.
|
boolean |
getPostPrunningOnlyGreaterClasses() |
Get the value of postPrunningOnlyGreaterClasses.
|
java.lang.String |
getRevision() |
|
weka.core.SelectedTag |
getRuleQualityMeasure() |
Gets the rule quality measure used.
|
weka.core.SelectedTag |
getRulesType() |
Gets the used approximation
|
weka.core.SelectedTag |
getSelectionCriterion() |
Gets the method used.
|
boolean |
getUsePartialMatching() |
Get the value of usePartialMatching.
|
boolean |
getUsePostPrunning() |
Get the value of usePostPrunning.
|
boolean |
getUseVariableConsitency() |
Get the value of useVariableConsitency.
|
weka.core.SelectedTag |
getVcMeasure() |
Gets the used vc measure
|
double |
getWorstEval() |
gets the worst possible evaluation for given criterion of selection
|
java.lang.String |
globalInfo() |
This method returns global info about this class.
|
boolean |
IsBetterEval(double eval1,
double eval2) |
gets result of comparison two evaluations
|
boolean |
isFilterImbalancedInstances() |
|
java.util.Enumeration |
listOptions() |
Returns an enumeration describing the available options.
|
static void |
main(java.lang.String[] args) |
Main method for testing this class.
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java.lang.String |
postPruningTypeTipText() |
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java.lang.String |
postPrunningCoefficientTipText() |
Returns the tip text for this property
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java.lang.String |
postPrunningOnlyGreaterClassesTipText() |
Returns the tip text for this property
|
void |
removeUnusedRules(java.util.BitSet positiveExamples,
java.util.BitSet negativeExamples,
int classValue) |
|
java.lang.String |
ruleQualityMeasureTipText() |
|
java.lang.String |
rulesTypeTipText() |
|
java.lang.String |
selectionCriterionTipText() |
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void |
setClassificationStrategy(weka.core.SelectedTag newClassificationStrategy) |
Sets the classification strategy used.
|
void |
setFilterImbalancedInstances(boolean filterImbalancedInstances) |
|
void |
setForwardPrunningCoefficient(double newForwardPrunningCoefficient) |
Set the value of forwardPrunningCoefficient.
|
void |
setOptions(java.lang.String[] options) |
Parses a given list of options.
|
void |
setPostPruningType(weka.core.SelectedTag newPostPruningType) |
Sets the post-pruning type used.
|
void |
setPostPrunningCoefficient(double newPostPrunningCoefficient) |
Set the value of post-pruning coefficient.
|
void |
setPostPrunningOnlyGreaterClasses(boolean newPostPrunningOnlyGreaterClasses) |
Set the value of postPrunningOnlyGreaterClasses.
|
void |
setRuleQualityMeasure(weka.core.SelectedTag newRuleQualityMeasure) |
Sets the rule quality measure used.
|
void |
setRulesType(weka.core.SelectedTag newRulesType) |
Sets the used approximation.
|
void |
setSelectionCriterion(weka.core.SelectedTag newSelectionCriterion) |
Sets the method used.
|
void |
setUsePartialMatching(boolean newUsePartialMatching) |
Set the value of usePartialMatching.
|
void |
setUsePostPrunning(boolean newUsePostPrunning) |
Set the value of usePostPrunning.
|
void |
setUseVariableConsitency(boolean newUseVariableConsitency) |
Set the value of useVariableConsitency.
|
void |
setVcMeasure(weka.core.SelectedTag newVcMeasure) |
Sets the used vc measure.
|
java.lang.String |
toString() |
|
void |
updateSelectorsAfterRule() |
|
void |
updateSelectorsAfterSelector(VCMLSelector lastBest) |
|
java.lang.String |
usePartialMatchingTipText() |
Returns the tip text for this property.
|
java.lang.String |
usePostPrunningTipText() |
Returns the tip text for this property.
|
java.lang.String |
useVariableConsitencyTipText() |
Returns the tip text for this property
|
java.lang.String |
vcMeasureTipText() |
public static int iFold
public static final weka.core.Tag[] TAGS_SELECTION_CRITERION
public static final weka.core.Tag[] TAGS_RULES_TYPE
public static final weka.core.Tag[] TAGS_VCMEASURE_TYPE
public static final weka.core.Tag[] TAGS_CLASSIFICATION_STRATEGY
public static final weka.core.Tag[] TAGS_POST_PRUNING_TYPE
public static final weka.core.Tag[] TAGS_RULE_QUALITY
public java.lang.String globalInfo()
public java.lang.String forwardPrunningCoefficientTipText()
public double getForwardPrunningCoefficient()
public void setForwardPrunningCoefficient(double newForwardPrunningCoefficient)
newForwardPrunningCoefficient
- Value to assign to forwardPrunningCoefficient.public java.lang.String usePostPrunningTipText()
public boolean getUsePostPrunning()
public void setUsePostPrunning(boolean newUsePostPrunning)
newUsePostPrunning
- Value to assign to usePostPrunning.public java.lang.String usePartialMatchingTipText()
public boolean getUsePartialMatching()
public void setUsePartialMatching(boolean newUsePartialMatching)
newUsePartialMatching
- Value to assign to usePartialMatching.public java.lang.String postPrunningOnlyGreaterClassesTipText()
public boolean getPostPrunningOnlyGreaterClasses()
public void setPostPrunningOnlyGreaterClasses(boolean newPostPrunningOnlyGreaterClasses)
newPostPrunningOnlyGreaterClasses
- Value to assign to postPrunningOnlyGreaterClasses.public java.lang.String useVariableConsitencyTipText()
public boolean getUseVariableConsitency()
public void setUseVariableConsitency(boolean newUseVariableConsitency)
newUseVariableConsitency
- Value to assign to useVariableConsitency.public java.lang.String postPrunningCoefficientTipText()
public double getPostPrunningCoefficient()
public void setPostPrunningCoefficient(double newPostPrunningCoefficient)
newPostPrunningCoefficient
- value to assign to postPrunningCoefficient.public java.lang.String selectionCriterionTipText()
public weka.core.SelectedTag getSelectionCriterion()
public void setSelectionCriterion(weka.core.SelectedTag newSelectionCriterion)
newSelectionCriterion
- the new selection criterion.public java.lang.String rulesTypeTipText()
public weka.core.SelectedTag getRulesType()
public void setRulesType(weka.core.SelectedTag newRulesType)
newRulesType
- the new rulesType.public java.lang.String vcMeasureTipText()
public weka.core.SelectedTag getVcMeasure()
public void setVcMeasure(weka.core.SelectedTag newVcMeasure)
newVcMeasure
- the new vcMeasure.public java.lang.String classificationStrategyTipText()
public weka.core.SelectedTag getClassificationStrategy()
public void setClassificationStrategy(weka.core.SelectedTag newClassificationStrategy)
newClassificationStrategy
- the new classificationStrategy.public java.lang.String postPruningTypeTipText()
public weka.core.SelectedTag getPostPruningType()
public void setPostPruningType(weka.core.SelectedTag newPostPruningType)
newPostPruningType
- -
the new postPruningType.public java.lang.String ruleQualityMeasureTipText()
public weka.core.SelectedTag getRuleQualityMeasure()
public void setRuleQualityMeasure(weka.core.SelectedTag newRuleQualityMeasure)
newRuleQualityMeasure
- the new rule quality measure.public boolean isFilterImbalancedInstances()
public void setFilterImbalancedInstances(boolean filterImbalancedInstances)
filterImbalancedInstances
- the filterImbalancedInstances to setpublic double getWorstEval()
public double getBestEval()
public boolean IsBetterEval(double eval1, double eval2)
eval1
- first evaluationeval2
- second evaluationpublic double Eval(java.util.BitSet subSet1, java.util.BitSet subSet2)
public double EvalLaplace(java.util.BitSet subSet1, java.util.BitSet subSet2)
public double EvalEntropy(java.util.BitSet subSet1, java.util.BitSet subSet2)
public VCMLSelector findBestNominalSelector(int iSelectors)
public VCMLSelector findBestNumericalSelector(int iSelectors)
public VCMLSelector findBestSelector()
public void generateNominalSelectors(int attrNum)
public void generateNumericalSelectors(int attrNum)
public void generateSelectors()
public void updateSelectorsAfterRule()
public void updateSelectorsAfterSelector(VCMLSelector lastBest)
public boolean areTwoInstancesEqual(int firstInstance, int secondInstance)
firstInstance
- -
index of the first instance in dataset to be checkedsecondInstance
- -
index of the second instance in dataset to be checkedpublic void generateApproximation()
public double classifyInstance(weka.core.Instance instance) throws java.lang.Exception
classifyInstance
in class weka.classifiers.Classifier
instance
- the instance to be classifiedjava.lang.Exception
- if an error occurred during the predictionpublic double[] distributionForInstance(weka.core.Instance instance) throws java.lang.Exception
distributionForInstance
in class weka.classifiers.Classifier
instance
- the instance to be classifiedjava.lang.Exception
- if distribution could not be computed successfullypublic void removeUnusedRules(java.util.BitSet positiveExamples, java.util.BitSet negativeExamples, int classValue)
public void buildClassifier(weka.core.Instances dataset) throws java.lang.Exception
buildClassifier
in class weka.classifiers.Classifier
java.lang.Exception
public java.lang.String toString()
toString
in class java.lang.Object
public java.lang.String getRevision()
public java.util.Enumeration listOptions()
listOptions
in class weka.classifiers.Classifier
public void setOptions(java.lang.String[] options) throws java.lang.Exception
setOptions
in class weka.classifiers.Classifier
options
- the list of options as an array of stringsjava.lang.Exception
- if an option is not supportedpublic java.lang.String[] getOptions()
getOptions
in class weka.classifiers.Classifier
public weka.core.Capabilities getCapabilities()
getCapabilities
in class weka.classifiers.Classifier
public static void main(java.lang.String[] args)
args
- the options