Examples with Beam edu.stanford.nlp.util.Beam used on opensource projects

Example 1 with Beam

use of edu.stanford.nlp.util.Beam in project CoreNLP by stanfordnlp.

the class BeamBestSequenceFinder method bestSequence.

public int[] bestSequence(SequenceModel ts, int size) {
    // Set up tag options
    int length = ts.length();
    int leftWindow = ts.leftWindow();
    int rightWindow = ts.rightWindow();
    int padLength = length + leftWindow + rightWindow;
    int[][] tags = new int[padLength][];
    int[] tagNum = new int[padLength];
    for (int pos = 0; pos < padLength; pos++) {
        tags[pos] = ts.getPossibleValues(pos);
        tagNum[pos] = tags[pos].length;
    }
    Beam newBeam = new Beam(beamSize, ScoredComparator.ASCENDING_COMPARATOR);
    TagSeq initSeq = new TagSeq();
    newBeam.add(initSeq);
    for (int pos = 0; pos < padLength; pos++) {
        if (Thread.interrupted()) {
            // Allow interrupting
            throw new RuntimeInterruptedException();
        }
        //System.out.println("scoring word " + pos + " / " + (leftWindow + length) + ", tagNum = " + tagNum[pos] + "...");
        //System.out.flush();
        Beam oldBeam = newBeam;
        if (pos < leftWindow + rightWindow && exhaustiveStart) {
            newBeam = new Beam(100000, ScoredComparator.ASCENDING_COMPARATOR);
        } else {
            newBeam = new Beam(beamSize, ScoredComparator.ASCENDING_COMPARATOR);
        }
        // each hypothesis gets extended and beamed
        for (Object anOldBeam : oldBeam) {
            if (Thread.interrupted()) {
                // Allow interrupting
                throw new RuntimeInterruptedException();
            }
            // System.out.print("#"); System.out.flush();
            TagSeq tagSeq = (TagSeq) anOldBeam;
            for (int nextTagNum = 0; nextTagNum < tagNum[pos]; nextTagNum++) {
                TagSeq nextSeq = tagSeq.tclone();
                if (pos >= leftWindow + rightWindow) {
                    nextSeq.extendWith(tags[pos][nextTagNum], ts, size);
                } else {
                    nextSeq.extendWith(tags[pos][nextTagNum]);
                }
                //System.out.println("Created: "+nextSeq.score()+" %% "+arrayToString(nextSeq.tags(), nextSeq.size()));
                newBeam.add(nextSeq);
            //		System.out.println("Beam size: "+newBeam.size()+" of "+beamSize);
            //System.out.println("Best is: "+((Scored)newBeam.iterator().next()).score());
            }
        }
        // System.out.println(" done");
        if (recenter) {
            double max = Double.NEGATIVE_INFINITY;
            for (Object aNewBeam1 : newBeam) {
                TagSeq tagSeq = (TagSeq) aNewBeam1;
                if (tagSeq.score > max) {
                    max = tagSeq.score;
                }
            }
            for (Object aNewBeam : newBeam) {
                TagSeq tagSeq = (TagSeq) aNewBeam;
                tagSeq.score -= max;
            }
        }
    }
    try {
        TagSeq bestSeq = (TagSeq) newBeam.iterator().next();
        int[] seq = bestSeq.tags();
        return seq;
    } catch (NoSuchElementException e) {
        log.info("Beam empty -- no best sequence.");
        return null;
    }
/*
    int[] tempTags = new int[padLength];

    // Set up product space sizes
    int[] productSizes = new int[padLength];

    int curProduct = 1;
    for (int i=0; i<leftWindow+rightWindow; i++)
      curProduct *= tagNum[i];
    for (int pos = leftWindow+rightWindow; pos < padLength; pos++) {
      if (pos > leftWindow+rightWindow)
	curProduct /= tagNum[pos-leftWindow-rightWindow-1]; // shift off
      curProduct *= tagNum[pos]; // shift on
      productSizes[pos-rightWindow] = curProduct;
    }

    // Score all of each window's options
    double[][] windowScore = new double[padLength][];
    for (int pos=leftWindow; pos<leftWindow+length; pos++) {
      windowScore[pos] = new double[productSizes[pos]];
      Arrays.fill(tempTags,tags[0][0]);
      for (int product=0; product<productSizes[pos]; product++) {
	int p = product;
	int shift = 1;
	for (int curPos = pos+rightWindow; curPos >= pos-leftWindow; curPos--) {
	  tempTags[curPos] = tags[curPos][p % tagNum[curPos]];
	  p /= tagNum[curPos];
	  if (curPos > pos)
	    shift *= tagNum[curPos];
	}
	if (tempTags[pos] == tags[pos][0]) {
	  // get all tags at once
	  double[] scores = ts.scoresOf(tempTags, pos);
	  // fill in the relevant windowScores
	  for (int t = 0; t < tagNum[pos]; t++) {
	    windowScore[pos][product+t*shift] = scores[t];
	  }
	}
      }
    }


    // Set up score and backtrace arrays
    double[][] score = new double[padLength][];
    int[][] trace = new int[padLength][];
    for (int pos=0; pos<padLength; pos++) {
      score[pos] = new double[productSizes[pos]];
      trace[pos] = new int[productSizes[pos]];
    }

    // Do forward Viterbi algorithm

    // loop over the classification spot
    //log.info();
    for (int pos=leftWindow; pos<length+leftWindow; pos++) {
      //log.info(".");
      // loop over window product types
      for (int product=0; product<productSizes[pos]; product++) {
	// check for initial spot
	if (pos==leftWindow) {
	  // no predecessor type
	  score[pos][product] = windowScore[pos][product];
	  trace[pos][product] = -1;
	} else {
	  // loop over possible predecessor types
	  score[pos][product] = Double.NEGATIVE_INFINITY;
	  trace[pos][product] = -1;
	  int sharedProduct = product / tagNum[pos+rightWindow];
	  int factor = productSizes[pos] / tagNum[pos+rightWindow];
	  for (int newTagNum=0; newTagNum<tagNum[pos-leftWindow-1]; newTagNum++) {
	    int predProduct = newTagNum*factor+sharedProduct;
	    double predScore = score[pos-1][predProduct]+windowScore[pos][product];
	    if (predScore > score[pos][product]) {
	      score[pos][product] = predScore;
	      trace[pos][product] = predProduct;
	    }
	  }
	}
      }
    }

    // Project the actual tag sequence
    double bestFinalScore = Double.NEGATIVE_INFINITY;
    int bestCurrentProduct = -1;
    for (int product=0; product<productSizes[leftWindow+length-1]; product++) {
      if (score[leftWindow+length-1][product] > bestFinalScore) {
	bestCurrentProduct = product;
	bestFinalScore = score[leftWindow+length-1][product];
      }
    }
    int lastProduct = bestCurrentProduct;
    for (int last=padLength-1; last>=length-1; last--) {
      tempTags[last] = tags[last][lastProduct % tagNum[last]];
      lastProduct /= tagNum[last];
    }
    for (int pos=leftWindow+length-2; pos>=leftWindow; pos--) {
      int bestNextProduct = bestCurrentProduct;
      bestCurrentProduct = trace[pos+1][bestNextProduct];
      tempTags[pos-leftWindow] = tags[pos-leftWindow][bestCurrentProduct / (productSizes[pos]/tagNum[pos-leftWindow])];
    }
    return tempTags;
    */
}