Examples of cc.mallet.fst.CRFOptimizableByBatchLabelLikelihood

cc.mallet.fst.CRFOptimizableByBatchLabelLikelihood

Implements label likelihood gradient computations for batches of data, can be easily parallelized.

The gradient computations are the same as that of CRFOptimizableByLabelLikelihood.

*Note*: Expectations corresponding to each batch of data can be computed in parallel. During gradient computation, the prior and the constraints are incorporated into the expectations of the last batch (see getBatchValue, getBatchValueGradient). *Note*: This implementation ignores instances with infinite weights (see getExpectationValue). @author Gaurav Chandalia

    if (numThreads == 1) {
      optLikelihood = new CRFOptimizableByLabelLikelihood(crf,trainingSet);
      ((CRFOptimizableByLabelLikelihood)optLikelihood).setGaussianPriorVariance(gpv);
    }
    else {
      CRFOptimizableByBatchLabelLikelihood likelihood = new CRFOptimizableByBatchLabelLikelihood(crf,trainingSet,numThreads);
      optLikelihood = new ThreadedOptimizable(likelihood,trainingSet,crf.getParameters().getNumFactors(),
        new CRFCacheStaleIndicator(crf));
      likelihood.setGaussianPriorVariance(gpv);
    }


    CRFOptimizableByGE ge = new CRFOptimizableByGE(crf,constraints,unlabeledSet,map,numThreads,geWeight);
    // turn off the prior... it already appears above!
    ge.setGaussianPriorVariance(Double.POSITIVE_INFINITY);

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Examples of cc.mallet.fst.CRFOptimizableByBatchLabelLikelihood

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