List of scoresOf() Examples

Examples of scoresOf()

edu.stanford.nlp.sequences.SequenceModel.scoresOf()

Computes the scores of labels for the element at the given position in the sequence, conditioned on the values of the labels at all other positions of the provided sequence. The returned array elements correspond index-by-index to the possible values returned by getPossibleValues(int). The label at sequence[position] is ignored in the calculations. The scores are often given as an unnormalized log conditional distribution over possible labels. Otherwise, they may be probabilities. If it is a probability distribution, conceptually, the scores should sum to 1 (perhaps after transforming them). Note that implementations often alter sequence[position] to do their calculations, and so access to the array isn't threadsafe, but should restore its value afterwards. @param sequence The sequence containing the rest of the values to condition on @param position The position of the element to give a distribution for @return The scores of the possible tokens at the specified position in the sequence

          }


          // score as nongene
          double bgScore = 0.0;
          for (j = start; j < end; j++) {
            double[] scores = ts.scoresOf(copy, j);
            scores = Scorer.recenter(scores);
            bgScore += scores[bgTag];
          }


          // first pass, compute all of the scores

            for (int k = j; k < end; k++) {
              copy[k] = ngTag;
              // score the sequence
              double ngScore = 0.0;
              for (int m = start; m < end; m++) {
                double[] scores = ts.scoresOf(copy, m);
                scores = Scorer.recenter(scores);
                ngScore += scores[tags[m]];
              }
              prevScores.incrementCount(new Pair<Integer,Integer>(Integer.valueOf(j), Integer.valueOf(k)), ngScore - bgScore);
            }

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Examples of scoresOf()

Examples of edu.stanford.nlp.sequences.SequenceModel.scoresOf()

Examples of edu.stanford.nlp.sequences.SequenceModel.scoresOf()