calhoun.analysis.crf.features.interval13
Class ReferenceBasePredictorInterval13Base

java.lang.Object
  calhoun.analysis.crf.AbstractFeatureManager<java.lang.Character>
      calhoun.analysis.crf.features.interval13.ReferenceBasePredictorInterval13Base

All Implemented Interfaces:

FeatureManager<java.lang.Character>, java.io.Serializable

Direct Known Subclasses:

ReferenceBasePredictorInterval13, ReferenceBasePredictorNodeOnlyInterval13, ReferenceBasePredictorZeroPadInterval13

public abstract class ReferenceBasePredictorInterval13Base
extends AbstractFeatureManager<java.lang.Character>

See Also:

Serialized Form

Constructor Summary

ReferenceBasePredictorInterval13Base()

Method Summary

void	evaluateNode(InputSequence<? extends java.lang.Character> seq, int pos, int state, FeatureList result)
java.lang.String	getFeatureName(int featureIndex) Returns a human identifiable name for the feature referenced by a given index.
int	getNumFeatures() Returns the number of features maintained by this FeatureManager.
boolean	isMultipleFeatures() if true, a separate feature index is used for each state, creating 13 weights instead of 1.
void	setMultipleFeatures(boolean weightPerState)
void	train(int startingIndex, ModelManager modelInfo, java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data) Provides access to the entire training set so that FeatureManager can compute global properties and assign feature indices.

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

Constructor Detail

ReferenceBasePredictorInterval13Base

public ReferenceBasePredictorInterval13Base()

Method Detail

getFeatureName

public java.lang.String getFeatureName(int featureIndex)

Description copied from interface: FeatureManager

Returns a human identifiable name for the feature referenced by a given index. Used for display purposes only.

Parameters:

featureIndex - the index of this feature

Returns:

the human readable name of this feature

getNumFeatures

public int getNumFeatures()

Description copied from interface: FeatureManager

Returns the number of features maintained by this FeatureManager. This number must be fixed after the call to trainFeatures is complete.

Returns:

number of features managed by this FeatureManager

train

public void train(int startingIndex,
                  ModelManager modelInfo,
                  java.util.List<? extends TrainingSequence<? extends java.lang.Character>> data)

Description copied from interface: FeatureManager

Provides access to the entire training set so that FeatureManager can compute global properties and assign feature indices. This will be called before any evaluations are requested. If the FeatureManager can have a variable number of features, this must be fixed within this method.

Parameters:

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 feature

data - the full list of training sequences to use to train the feature

evaluateNode

public void evaluateNode(InputSequence<? extends java.lang.Character> seq,
                         int pos,
                         int state,
                         FeatureList result)

isMultipleFeatures

public boolean isMultipleFeatures()

if true, a separate feature index is used for each state, creating 13 weights instead of 1.

Returns:

returns true if a separate feature index is used for each state

setMultipleFeatures

public void setMultipleFeatures(boolean weightPerState)

Parameters:

multipleFeatures - The multipleFeatures to set.