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])));