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java.lang.Objectcalhoun.analysis.crf.AbstractFeatureManager<java.lang.Character>
calhoun.analysis.crf.features.tricycle13.GeneConstraints
public class GeneConstraints
lmplements basic constraints on gene calls.
| Constructor Summary | |
|---|---|
GeneConstraints()
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| Method Summary | |
|---|---|
void |
evaluateEdge(InputSequence<? extends java.lang.Character> seq,
int pos,
int prevState,
int state,
FeatureList result)
Evaluates the set of features managed by this object for the given arguments. |
java.lang.String |
getFeatureName(int featureIndex)
Returns a human identifiable name for the feature referenced by a given index. |
int |
getNumFeatures()
This is a constraint class, so we don't return features |
void |
train(int startingIndex,
ModelManager modelInfo,
java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)
Set up the matrix Depends on states starting with the words 'intergenic, intron, and exon'. |
| Methods inherited from class calhoun.analysis.crf.AbstractFeatureManager |
|---|
getCacheStrategy, getInputComponent, setInputComponent |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Methods inherited from interface calhoun.analysis.crf.FeatureManager |
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getCacheStrategy, getInputComponent, setInputComponent |
| Constructor Detail |
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public GeneConstraints()
| Method Detail |
|---|
public java.lang.String getFeatureName(int featureIndex)
FeatureManager
getFeatureName in interface FeatureManager<java.lang.Character>featureIndex - the index of this feature
public int getNumFeatures()
getNumFeatures in interface FeatureManager<java.lang.Character>FeatureManager
public void train(int startingIndex,
ModelManager modelInfo,
java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)
train in interface FeatureManager<java.lang.Character>startingIndex - the feature index of the first feature owned by this FeatureManager. Each FeatureManager
must use up consecutive indexes, so the last index used will be startingIndex + numFeatures - 1.modelInfo - the model that contains this featuredata - the full list of training sequences to use to train the feature
public void evaluateEdge(InputSequence<? extends java.lang.Character> seq,
int pos,
int prevState,
int state,
FeatureList result)
FeatureManagerEdge
evaluateEdge in interface FeatureManagerEdge<java.lang.Character>
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