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Stanford CoreNLP provides a set of natural language analysis tools which can take raw English language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases and word dependencies, and indicate which noun phrases refer to the same entities. It provides the foundational building blocks for higher level text understanding applications.

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package edu.stanford.nlp.parser.shiftreduce;

import java.io.Serializable;

import edu.stanford.nlp.util.ArrayUtils;

/**
 * Stores one row of the sparse matrix which makes up the multiclass perceptron.
 * 

 * Uses a lot of bit fiddling to get the desired results.  What we
 * want is a row of scores representing transitions where each score
 * is the score for that transition (for the feature using this Weight
 * object).  Since the average model seems to have about 3 non-zero
 * scores per feature, we condense that by keeping pairs of index and
 * score.  However, we can then further condense that by bit packing
 * the index and score into one long.  This cuts down on object
 * creation and makes it faster to read/write the models.
 * 

 * Thankfully, all of the unpleasant bit fiddling can be hidden away
 * in this one class.
 *
 * @author John Bauer
 */

public class Weight implements Serializable {
  public Weight() {
    packed = null;
  }

  public Weight(Weight other) {
    if (other.size() == 0) {
      packed = null;
      return;
    }
    packed = ArrayUtils.copy(other.packed);
    condense();
  }

  public int size() {
    if (packed == null) {
      return 0;
    }
    return packed.length;
  }

  private int unpackIndex(int i) {
    long pack = packed[i];
    return (int) (pack >>> 32);
  }

  private float unpackScore(int i) {
    long pack = packed[i];
    return Float.intBitsToFloat((int) (pack & 0xFFFFFFFF));
  }

  private long pack(int index, float score) {
    long pack = ((long) (Float.floatToIntBits(score))) & 0x00000000FFFFFFFFL;
    pack = pack | (((long) index) << 32);
    return pack;
  }

  public void score(float[] scores) {
    for (int i = 0; i < size(); ++i) {
      // Since this is the critical method, we optimize it even further.
      // We could do this:
      // int index = unpackIndex; float score = unpackScore;
      // That results in an extra array lookup
      final long pack = packed[i];
      final int index = (int) (pack >>> 32);
      final float score = Float.intBitsToFloat((int) (pack & 0xFFFFFFFF));
      scores[index] += score;
    }
  }

  public void addScaled(Weight other, float scale) {
    for (int i = 0; i < other.size(); ++i) {
      int index = other.unpackIndex(i);
      float score = other.unpackScore(i);
      updateWeight(index, score * scale);
    }
  }

  public void condense() {
    if (packed == null) {
      return;
    }

    int nonzero = 0;
    for (int i = 0; i < packed.length; ++i) {
      if (unpackScore(i) != 0.0f) {
        ++nonzero;
      }
    }

    if (nonzero == 0) {
      packed = null;
      return;
    }

    if (nonzero == packed.length) {
      return;
    }

    long[] newPacked = new long[nonzero];
    int j = 0;
    for (int i = 0; i < packed.length; ++i) {
      if (unpackScore(i) == 0.0f) {
        continue;
      }
      int index = unpackIndex(i);
      float score = unpackScore(i);
      newPacked[j] = pack(index, score);
      ++j;
    }
    packed = newPacked;
  }

  public void updateWeight(int index, float increment) {
    if (index < 0) {
      return;
    }

    if (packed == null) {
      packed = new long[1];
      packed[0] = pack(index, increment);
      return;
    }

    for (int i = 0; i < packed.length; ++i) {
      if (unpackIndex(i) == index) {
        float score = unpackScore(i);
        packed[i] = pack(index, score + increment);
        return;
      }
    }

    long[] newPacked = new long[packed.length + 1];
    for (int i = 0; i < packed.length; ++i) {
      newPacked[i] = packed[i];
    }
    newPacked[packed.length] = pack(index, increment);
    packed = newPacked;
  }

  long[] packed;

  private static final long serialVersionUID = 1;
}