opennlp.model.MutableContext.updateParameter java code examples

Common ways to obtain MutableContext

private void myMethod () {
 MutableContext m =new MutableContext(outcomePattern, new double[numActiveOutcomes])
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}

  modelExpects[threadIndex][pi].updateParameter(aoi, modelDistribution[oi] * trainingDataFeatValues[ei][j] * numTimesEventsSeen[ei]);
} else {
  modelExpects[threadIndex][pi].updateParameter(aoi, modelDistribution[oi] * numTimesEventsSeen[ei]);

 modelExpects[threadIndex][pi].updateParameter(aoi, modelDistribution[oi] * values[ei][j] * numTimesEventsSeen[ei]);
} else {
 modelExpects[threadIndex][pi].updateParameter(aoi, modelDistribution[oi] * numTimesEventsSeen[ei]);

    modelExpects[0][pi].updateParameter(aoi, modelExpects[i][pi].getParameters()[aoi]);
for (int aoi = 0; aoi < activeOutcomes.length; aoi++) {
  if (useGaussianSmoothing) {
    params[pi].updateParameter(aoi, gaussianUpdate(pi, aoi, correctionConstant));
  } else {
    if (model[aoi] == 0) {
      LOG.error("Model expects == 0 for " + featNames[pi] + " " + labels[aoi]);
    params[pi].updateParameter(aoi, ((Math.log(observed[aoi]) - Math.log(model[aoi])) / correctionConstant));

modelExpects[threadIndex][pi].updateParameter(aoi,modelDistribution[oi] * values[ei][j] * numTimesEventsSeen[ei]);
modelExpects[threadIndex][pi].updateParameter(aoi,modelDistribution[oi] * numTimesEventsSeen[ei]);

modelExpects[threadIndex][pi].updateParameter(aoi,modelDistribution[oi] * values[ei][j] * numTimesEventsSeen[ei]);
modelExpects[threadIndex][pi].updateParameter(aoi,modelDistribution[oi] * numTimesEventsSeen[ei]);

  modelExpects[0][pi].updateParameter(aoi, modelExpects[i][pi].getParameters()[aoi]);
for (int aoi=0;aoi<activeOutcomes.length;aoi++) {
 if (useGaussianSmoothing) {
  params[pi].updateParameter(aoi,gaussianUpdate(pi,aoi,numEvents,correctionConstant));
  params[pi].updateParameter(aoi,((Math.log(observed[aoi]) - Math.log(model[aoi]))/correctionConstant));

  modelExpects[0][pi].updateParameter(aoi, modelExpects[i][pi].getParameters()[aoi]);
for (int aoi=0;aoi<activeOutcomes.length;aoi++) {
 if (useGaussianSmoothing) {
  params[pi].updateParameter(aoi,gaussianUpdate(pi,aoi,numEvents,correctionConstant));
  params[pi].updateParameter(aoi,((Math.log(observed[aoi]) - Math.log(model[aoi]))/correctionConstant));

  modelExpects[0][pi].updateParameter(aoi, modelExpects[i][pi].getParameters()[aoi]);
for (int aoi = 0; aoi < activeOutcomes.length; aoi++) {
 if (useGaussianSmoothing) {
  params[pi].updateParameter(aoi, gaussianUpdate(pi, aoi, numEvents, correctionConstant));
 } else {
  if (model[aoi] == 0) {
  params[pi].updateParameter(aoi, ((Math.log(observed[aoi]) - Math.log(model[aoi])) / correctionConstant));

   int pi = contexts[ei][ci];
   if (values == null) {
    params[pi].updateParameter(targetOutcome, stepsize);
    params[pi].updateParameter(maxOutcome, -stepsize);
   } else {
    params[pi].updateParameter(targetOutcome, stepsize*values[ei][ci]);
    params[pi].updateParameter(maxOutcome, -stepsize*values[ei][ci]);
for (int pi = 0; pi < numPreds; pi++) 
 for (int aoi=0;aoi<numOutcomes;aoi++)
  summedParams[pi].updateParameter(aoi, params[pi].getParameters()[aoi]);

   int pi = contexts[ei][ci];
   if (values == null) {
    params[pi].updateParameter(targetOutcome, stepsize);
    params[pi].updateParameter(maxOutcome, -stepsize);
   } else {
    params[pi].updateParameter(targetOutcome, stepsize*values[ei][ci]);
    params[pi].updateParameter(maxOutcome, -stepsize*values[ei][ci]);
for (int pi = 0; pi < numPreds; pi++) 
 for (int aoi=0;aoi<numOutcomes;aoi++)
  summedParams[pi].updateParameter(aoi, params[pi].getParameters()[aoi]);

if (pi != -1) {
 params[pi].updateParameter(oi, featureCounts[oi].get(feature));
 if (useAverage) {
  if (updates[pi][oi][VALUE] != 0) {
   averageParams[pi].updateParameter(oi,updates[pi][oi][VALUE]*(numSequences*(iteration-updates[pi][oi][ITER])+(si-updates[pi][oi][EVENT])));

if (pi != -1) {
 params[pi].updateParameter(oi, featureCounts[oi].get(feature));
 if (useAverage) {
  if (updates[pi][oi][VALUE] != 0) {
   averageParams[pi].updateParameter(oi,updates[pi][oi][VALUE]*(numSequences*(iteration-updates[pi][oi][ITER])+(si-updates[pi][oi][EVENT])));

Javadoc

Updated the parameter or expected value at the specified outcomeIndex by adding the specified value to its current value.

Popular methods of MutableContext

<init>
Creates a new parameters object with the specified parameters associated with the specified outcome
getOutcomes
getParameters
setParameter
Assigns the parameter or expected value at the specified outcomeIndex the specified value.

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How to use updateParametermethodin opennlp.model.MutableContext

Best Java code snippets using opennlp.model.MutableContext.updateParameter (Showing top 12 results out of 315)

How to use
updateParameter
method
in
opennlp.model.MutableContext