private double nextIteration()

in opennlp-tools/src/main/java/opennlp/tools/ml/maxent/GISTrainer.java [566:654]
62 lines of code
14 McCabe index (conditional complexity)

  private double nextIteration(double correctionConstant,
                               CompletionService<ModelExpectationComputeTask> completionService,
                               int iteration) {
    // compute contribution of p(a|b_i) for each feature and the new
    // correction parameter
    double loglikelihood = 0.0;
    int numEvents = 0;
    int numCorrect = 0;

    // Each thread gets equal number of tasks, if the number of tasks
    // is not divisible by the number of threads, the first "leftOver"
    // threads have one extra task.
    int numberOfThreads = modelExpects.length;
    int taskSize = numUniqueEvents / numberOfThreads;
    int leftOver = numUniqueEvents % numberOfThreads;

    // submit all tasks to the completion service.
    for (int i = 0; i < numberOfThreads; i++) {
      if (i < leftOver) {
        completionService.submit(new ModelExpectationComputeTask(i, i * taskSize + i,
            taskSize + 1));
      } else {
        completionService.submit(new ModelExpectationComputeTask(i,
            i * taskSize + leftOver, taskSize));
      }
    }

    for (int i = 0; i < numberOfThreads; i++) {
      ModelExpectationComputeTask finishedTask;
      try {
        finishedTask = completionService.take().get();
      } catch (InterruptedException e) {
        // TODO: We got interrupted, but that is currently not really supported!
        // For now we fail hard. We hopefully soon
        // handle this case properly!
        throw new IllegalStateException("Interruption is not supported!", e);
      } catch (ExecutionException e) {
        // Only runtime exception can be thrown during training, if one was thrown
        // it should be re-thrown. That could for example be a NullPointerException
        // which is caused through a bug in our implementation.
        throw new RuntimeException("Exception during training: " + e.getMessage(), e);
      }

      // When they are done, retrieve the results ...
      numEvents += finishedTask.getNumEvents();
      numCorrect += finishedTask.getNumCorrect();
      loglikelihood += finishedTask.getLoglikelihood();
    }

    // merge the results of the two computations
    for (int pi = 0; pi < numPreds; pi++) {
      int[] activeOutcomes = params[pi].getOutcomes();

      for (int aoi = 0; aoi < activeOutcomes.length; aoi++) {
        for (int i = 1; i < modelExpects.length; i++) {
          modelExpects[0][pi].updateParameter(aoi, modelExpects[i][pi].getParameters()[aoi]);
        }
      }
    }

    // compute the new parameter values
    for (int pi = 0; pi < numPreds; pi++) {
      double[] observed = observedExpects[pi].getParameters();
      double[] model = modelExpects[0][pi].getParameters();
      int[] activeOutcomes = params[pi].getOutcomes();
      for (int aoi = 0; aoi < activeOutcomes.length; aoi++) {
        if (useGaussianSmoothing) {
          params[pi].updateParameter(aoi, gaussianUpdate(pi, aoi, correctionConstant));
        } else {
          if (model[aoi] == 0) {
            logger.warn("Model expects == 0 for {} {}", predLabels[pi], outcomeLabels[aoi]);
          }
          //params[pi].updateParameter(aoi,(StrictMath.log(observed[aoi]) - StrictMath.log(model[aoi])));
          params[pi].updateParameter(aoi, ((StrictMath.log(observed[aoi]) - StrictMath.log(model[aoi]))
              / correctionConstant));
        }

        for (MutableContext[] modelExpect : modelExpects) {
          modelExpect[pi].setParameter(aoi, 0.0); // re-initialize to 0.0's
        }

      }
    }

    getTrainingProgressMonitor(trainingConfiguration).
        finishedIteration(iteration, numCorrect, numEvents, TrainingMeasure.LOG_LIKELIHOOD, loglikelihood);

    return loglikelihood;
  }