How to use

indexOf

method

in

opennlp.tools.ml.maxent.quasinewton.NegLogLikelihood

expectation[oi] = 0;
for (ai = 0; ai < contexts[ci].length; ai++) {
 vectorIndex = indexOf(oi, contexts[ci][ai]);
 predValue = values != null ? values[ci][ai] : 1.0;
 expectation[oi] += predValue * x[vectorIndex];
empirical = outcomeList[ci] == oi ? 1 : 0;
for (ai = 0; ai < contexts[ci].length; ai++) {
 vectorIndex = indexOf(oi, contexts[ci][ai]);
 predValue = values != null ? values[ci][ai] : 1.0;
 gradient[vectorIndex] +=

/**
 * Negative log-likelihood
 */
public double valueAt(double[] x) {
 if (x.length != dimension)
  throw new IllegalArgumentException(
    "x is invalid, its dimension is not equal to domain dimension.");
 int ci, oi, ai, vectorIndex, outcome;
 double predValue, logSumOfExps;
 double negLogLikelihood = 0;
 for (ci = 0; ci < numContexts; ci++) {
  for (oi = 0; oi < numOutcomes; oi++) {
   tempSums[oi] = 0;
   for (ai = 0; ai < contexts[ci].length; ai++) {
    vectorIndex = indexOf(oi, contexts[ci][ai]);
    predValue = values != null ? values[ci][ai] : 1.0;
    tempSums[oi] += predValue * x[vectorIndex];
   }
  }
  logSumOfExps = ArrayMath.logSumOfExps(tempSums);
  outcome = outcomeList[ci];
  negLogLikelihood -= (tempSums[outcome] - logSumOfExps) * numTimesEventsSeen[ci];
 }
 return negLogLikelihood;
}

expectation[oi] = 0;
for (ai = 0; ai < contexts[ci].length; ai++) {
 vectorIndex = indexOf(oi, contexts[ci][ai]);
 predValue = values != null ? values[ci][ai] : 1.0;
 expectation[oi] += predValue * x[vectorIndex];
empirical = outcomeList[ci] == oi ? 1 : 0;
for (ai = 0; ai < contexts[ci].length; ai++) {
 vectorIndex = indexOf(oi, contexts[ci][ai]);
 predValue = values != null ? values[ci][ai] : 1.0;
 gradient[vectorIndex] +=

expectation[oi] = 0;
for (ai = 0; ai < contexts[ci].length; ai++) {
 vectorIndex = indexOf(oi, contexts[ci][ai]);
 predValue = values != null ? values[ci][ai] : 1.0;
 expectation[oi] += predValue * x[vectorIndex];
empirical = outcomeList[ci] == oi ? 1 : 0;
for (ai = 0; ai < contexts[ci].length; ai++) {
 vectorIndex = indexOf(oi, contexts[ci][ai]);
 predValue = values != null ? values[ci][ai] : 1.0;
 gradient[vectorIndex] +=

/**
 * Negative log-likelihood
 */
public double valueAt(double[] x) {
 if (x.length != dimension)
  throw new IllegalArgumentException(
    "x is invalid, its dimension is not equal to domain dimension.");
 int ci, oi, ai, vectorIndex, outcome;
 double predValue, logSumOfExps;
 double negLogLikelihood = 0;
 for (ci = 0; ci < numContexts; ci++) {
  for (oi = 0; oi < numOutcomes; oi++) {
   tempSums[oi] = 0;
   for (ai = 0; ai < contexts[ci].length; ai++) {
    vectorIndex = indexOf(oi, contexts[ci][ai]);
    predValue = values != null ? values[ci][ai] : 1.0;
    tempSums[oi] += predValue * x[vectorIndex];
   }
  }
  logSumOfExps = ArrayMath.logSumOfExps(tempSums);
  outcome = outcomeList[ci];
  negLogLikelihood -= (tempSums[outcome] - logSumOfExps) * numTimesEventsSeen[ci];
 }
 return negLogLikelihood;
}

/**
 * Negative log-likelihood
 */
public double valueAt(double[] x) {
 if (x.length != dimension)
  throw new IllegalArgumentException(
    "x is invalid, its dimension is not equal to domain dimension.");
 int ci, oi, ai, vectorIndex, outcome;
 double predValue, logSumOfExps;
 double negLogLikelihood = 0;
 for (ci = 0; ci < numContexts; ci++) {
  for (oi = 0; oi < numOutcomes; oi++) {
   tempSums[oi] = 0;
   for (ai = 0; ai < contexts[ci].length; ai++) {
    vectorIndex = indexOf(oi, contexts[ci][ai]);
    predValue = values != null ? values[ci][ai] : 1.0;
    tempSums[oi] += predValue * x[vectorIndex];
   }
  }
  logSumOfExps = ArrayMath.logSumOfExps(tempSums);
  outcome = outcomeList[ci];
  negLogLikelihood -= (tempSums[outcome] - logSumOfExps) * numTimesEventsSeen[ci];
 }
 return negLogLikelihood;
}