Examples of CRFOptimizableByGradientValues

cc.mallet.fst.CRFOptimizableByGradientValues
A CRF objective function that is the sum of multiple objective functions that implement Optimizable.ByGradientValue. @author Gregory Druck @author Gaurav Chandalia

Examples of cc.mallet.fst.CRFOptimizableByGradientValues


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


    CRFOptimizableByGradientValues opt = 
      new CRFOptimizableByGradientValues(crf,new Optimizable.ByGradientValue[] { optLikelihood, ge });
    
    LimitedMemoryBFGS optimizer = new LimitedMemoryBFGS(opt);


    try {
      converged = optimizer.optimize(numIterations);

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Examples of cc.mallet.fst.CRFOptimizableByGradientValues

    CRFOptimizableByLabelLikelihood likelihood = new CRFOptimizableByLabelLikelihood(crf, labeled);
    likelihood.setGaussianPriorVariance(gaussianPriorVariance);
    CRFOptimizableByEntropyRegularization regularization = new CRFOptimizableByEntropyRegularization(crf, unlabeled);
    regularization.setScalingFactor(this.entRegScalingFactor);
    
    CRFOptimizableByGradientValues regLikelihood = new CRFOptimizableByGradientValues(crf,
        new Optimizable.ByGradientValue[] { likelihood, regularization} );
    this.bfgs = new LimitedMemoryBFGS(regLikelihood);
    converged = false;
    logger.info ("CRF about to train with "+numIterations+" iterations");
    // sometimes resetting the optimizer helps to find

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Examples of cc.mallet.fst.CRFOptimizableByGradientValues

    CRFOptimizableByLabelLikelihood likelihood = new CRFOptimizableByLabelLikelihood(crf, labeled);
    likelihood.setGaussianPriorVariance(gaussianPriorVariance);
    CRFOptimizableByEntropyRegularization regularization = new CRFOptimizableByEntropyRegularization(crf, unlabeled);
    regularization.setScalingFactor(this.entRegScalingFactor);
    
    CRFOptimizableByGradientValues regLikelihood = new CRFOptimizableByGradientValues(crf,
        new Optimizable.ByGradientValue[] { likelihood, regularization} );
    this.bfgs = new LimitedMemoryBFGS(regLikelihood);
    converged = false;
    logger.info ("CRF about to train with "+numIterations+" iterations");
    // sometimes resetting the optimizer helps to find

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