Package org.tribuo.classification.sequence.viterbi

Class ViterbiModel

java.lang.Object

org.tribuo.sequence.SequenceModel<Label>

org.tribuo.classification.sequence.viterbi.ViterbiModel

All Implemented Interfaces:

com.oracle.labs.mlrg.olcut.provenance.Provenancable<ModelProvenance>, Serializable

public class ViterbiModel
extends SequenceModel<Label>

An implementation of a viterbi model.

See Also:

• Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class	Description
static enum	ViterbiModel.ScoreAggregation	Types of label score aggregation.

Field Summary

Fields inherited from class org.tribuo.sequence.SequenceModel

featureIDMap, name, outputIDMap, provenanceOutput

Method Summary

Modifier and Type	Method	Description
ViterbiModel.ScoreAggregation	getScoreAggregation()	Gets the score aggregation function.
int	getStackSize()	Gets the stack size of this model.
Map<String,List<com.oracle.labs.mlrg.olcut.util.Pair<String,Double>>>	getTopFeatures(int n)	Gets the top n features associated with this model.
protected List<Label>	getTopLabels(Map<String,Label> distribution)
protected static List<Label>	getTopLabels(Map<String,Label> distribution, int stackSize)
List<List<Prediction<Label>>>	predict(SequenceDataset<Label> examples)	Uses the model to predict the labels for multiple examples contained in a data set.
List<Prediction<Label>>	predict(SequenceExample<Label> examples)	Uses the model to predict the output for a single example.

Methods inherited from class org.tribuo.sequence.SequenceModel

getFeatureIDMap, getName, getOutputIDInfo, getProvenance, predict, setName, toMaxLabels, toString, validate

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Method Details

predict

public List<List<Prediction<Label>>> predict(SequenceDataset<Label> examples)

Description copied from class: SequenceModel

Uses the model to predict the labels for multiple examples contained in a data set.

Overrides:

predict in class SequenceModel<Label>

Parameters:

examples - the data set containing the examples to predict.

Returns:

the results of the predictions, in the same order as the data set generates the example.

predict

public List<Prediction<Label>> predict(SequenceExample<Label> examples)

Description copied from class: SequenceModel

Uses the model to predict the output for a single example.

Specified by:

predict in class SequenceModel<Label>

Parameters:

examples - the example to predict.

Returns:

the result of the prediction.

getTopLabels

protected List<Label> getTopLabels(Map<String,Label> distribution)

getTopLabels

protected static List<Label> getTopLabels(Map<String,Label> distribution,
 int stackSize)

getStackSize

public int getStackSize()

Gets the stack size of this model.

Returns:

The stack size.

getScoreAggregation

public ViterbiModel.ScoreAggregation getScoreAggregation()

Gets the score aggregation function.

Returns:

The score aggregation function.

getTopFeatures

public Map<String,List<com.oracle.labs.mlrg.olcut.util.Pair<String,Double>>> getTopFeatures(int n)

Description copied from class: SequenceModel

Gets the top n features associated with this model.

If the model does not produce per output feature lists, it returns a map with a single element with key Model.ALL_OUTPUTS.

If the model cannot describe it's top features then it returns Collections.emptyMap().

Specified by:

getTopFeatures in class SequenceModel<Label>

Parameters:

n - the number of features to return. If this value is less than 0, all features should be returned for each class, unless the model cannot score it's features.

Returns:

a map from string outputs to an ordered list of pairs of feature names and weights associated with that feature in the model