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## calhoun.analysis.crf
Interface CRFInference

All Known Implementing Classes:
  SemiMarkovViterbi, SemiMarkovViterbiNoCache, Viterbi

    public interface CRFInference

an interface to inference algorithms for CRFs. Given a model, a set of feature weights, and a set of input data, the algorithm selects a sequence of hidden states. The Viterbi dynamic programming algorithm and its variants are usually used for this problem.

Nested Class Summary
`static class` | `CRFInference.InferenceResult` holder which contains the results of an inference run.

Method Summary
`CRFInference.InferenceResult` | `predict(ModelManager mm, InputSequence<?> data, double[] weights)` Return the labelling that maximizes the conditional probability P(y|x).

Method Detail

### predict

    CRFInference.InferenceResult predict(ModelManager mm,
                                         InputSequence<?> data,
                                         double[] weights)

  Return the labelling that maximizes the conditional probability P(y|x).

  Parameters:
    `mm` - model to use for training
    `data` - input sequence to label
    `weights` - 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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