Examples of edu.stanford.nlp.maxent.CGRunner.solveL1()

edu.stanford.nlp.maxent.CGRunner.solveL1()
Solves the problem using OWLQN. The solution is stored in the {@code lambda} array of {@code prob}. Note that the likelihood function will be a penalized L2 likelihood function unless you have turned this off via setting the priorSigmaS to 0.0. @param weight Controls the sparseness/regularization of the L1 solution.The bigger the number the sparser the solution. Weights between 0.01 and 1.0 typically give good performance.

    LambdaSolveTagger prob = new LambdaSolveTagger(p, 0.0001, fnumArr);
    maxentTagger.prob = prob;


    if (config.getSearch().equals("owlqn")) {
      CGRunner runner = new CGRunner(prob, config.getModel(), config.getSigmaSquared());
      runner.solveL1(config.getRegL1());
    } else if (config.getSearch().equals("owlqn2")) {
      CGRunner runner = new CGRunner(prob, config.getModel(), config.getSigmaSquared());
      runner.solveOWLQN2(config.getRegL1());
    } else if (config.getSearch().equals("cg")) {
      CGRunner runner = new CGRunner(prob, config.getModel(), config.getSigmaSquared());

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    LambdaSolveTagger prob = new LambdaSolveTagger(p, 0.0001, 0.00001);
    GlobalHolder.prob = prob;


    if (config.getSearch().equals("owlqn")) {
      CGRunner runner = new CGRunner(prob, config.getModel(), config.getSigmaSquared());
      runner.solveL1(config.getRegL1());
    } else if (config.getSearch().equals("cg")) {
      CGRunner runner = new CGRunner(prob, config.getModel(), config.getSigmaSquared());
      runner.solveCG();
    } else if (config.getSearch().equals("qn")) {
      CGRunner runner = new CGRunner(prob, config.getModel(), config.getSigmaSquared());

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