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## calhoun.analysis.crf.solver
Class SemiMarkovViterbi

    java.lang.Object
      calhoun.analysis.crf.solver.SemiMarkovViterbi

All Implemented Interfaces:
  CRFInference

    public class SemiMarkovViterbi

extends java.lang.Object
implements CRFInference

viterbi algorithm for semi-Markov CRFs. Uses a `CacheProcessor` to handle the complexities of evaluation. This is similar to a markov viterbi search, except we have to search over all valid segments to fill in one entry in the matrix instead of just looking at the last position.

Nested Class Summary

Nested classes/interfaces inherited from interface calhoun.analysis.crf.CRFInference
`CRFInference.InferenceResult`

Constructor Summary
`SemiMarkovViterbi()`

Method Summary
`CacheProcessor` | `getCacheProcessor()` gets the cache processor used to access feature evaluations
`CRFInference.InferenceResult` | `predict(ModelManager fm, InputSequence<?> seq, double[] lambda)` Return the labelling that maximizes the conditional probability P(y|x).
`void` | `setCacheProcessor(CacheProcessor cacheProcessor)` sets the cache processor used to access feature evaluations

Methods inherited from class java.lang.Object
`equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

Constructor Detail

### SemiMarkovViterbi

    public SemiMarkovViterbi()

Method Detail

### getCacheProcessor

    public CacheProcessor getCacheProcessor()

  gets the cache processor used to access feature evaluations

  Returns:
    the configured cache processor

### setCacheProcessor

    public void setCacheProcessor(CacheProcessor cacheProcessor)

  sets the cache processor used to access feature evaluations

  Parameters:
    `cacheProcessor` - the cache processor to use

### predict

    public CRFInference.InferenceResult predict(ModelManager fm,
                                                InputSequence<?> seq,
                                                double[] lambda)

  Description copied from interface: `CRFInference`
  Return the labelling that maximizes the conditional probability P(y|x).

  Specified by:
    `predict` in interface `CRFInference`
  Parameters:
    `fm` - model to use for training
    `seq` - input sequence to label
    `lambda` - array of feature weights. Usually these will be derived from a training pass.
  Returns:
    inference result containing the hidden states which are predicted and the score outputs.

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