Java Entry.getIntKey方法代码示例

import it.unimi.dsi.fastutil.ints.Int2DoubleMap.Entry; //导入方法依赖的package包/类
private Vector addToIndex(final Vector v) {
  double pscore = 0;
  Vector residual = new Vector(v.timestamp());
  for (Entry e : v.int2DoubleEntrySet()) {
    int dimension = e.getIntKey();
    double weight = e.getDoubleValue();
    pscore += weight * maxVector.get(dimension);
    if (Double.compare(pscore, theta) >= 0) {
      PostingList list;
      if ((list = idx.get(dimension)) == null) {
        list = new PostingList();
        idx.put(dimension, list);
      }
      list.add(v.timestamp(), weight);
      size++;
      // v.remove(dimension);
    } else {
      residual.put(dimension, weight);
    }
  }
  return residual;
}
 

import it.unimi.dsi.fastutil.ints.Int2DoubleMap.Entry; //导入方法依赖的package包/类
@Test
public void testIterationOrder() {
  Vector v = new Vector();
  v.put(0, 0);
  v.put(1, 1);
  v.put(2, 2);
  v.put(3, 3);
  v.put(4, 4);
  BidirectionalIterator<Int2DoubleMap.Entry> it = v.int2DoubleEntrySet().fastIterator(v.int2DoubleEntrySet().last());
  // for (int i = 0; i < v.size(); i++) {
  int i = 4;
  while (it.hasPrevious()) {
    Entry e = it.previous();
    int lastKey = e.getIntKey();
    double lastValue = e.getDoubleValue();
    assertEquals(i, lastKey);
    assertEquals(i, lastValue, Double.MIN_NORMAL);
    i--;
  }
  assertEquals(5, v.size());
  assertEquals(0, v.int2DoubleEntrySet().first().getIntKey());
  assertEquals(4, v.int2DoubleEntrySet().last().getIntKey());
}
 

import it.unimi.dsi.fastutil.ints.Int2DoubleMap.Entry; //导入方法依赖的package包/类
private final Long2DoubleOpenHashMap generateCandidates(final Vector v, final boolean addToIndex) {
  final Long2DoubleOpenHashMap accumulator = new Long2DoubleOpenHashMap(size);
  for (Entry e : v.int2DoubleEntrySet()) {
    final int dimension = e.getIntKey();
    final double queryWeight = e.getDoubleValue();
    PostingList list;
    if ((list = idx.get(dimension)) != null) {
      for (PostingEntry pe : list) {
        numPostingEntries++;
        final long targetID = pe.getID();
        final double targetWeight = pe.getWeight();
        final double additionalSimilarity = queryWeight * targetWeight;
        accumulator.addTo(targetID, additionalSimilarity);
      }
    }
    if (addToIndex) {
      if (list == null) {
        list = new PostingList();
        idx.put(dimension, list);
      }
      list.add(v.timestamp(), queryWeight);
      size++;
    }
  }
  numCandidates += accumulator.size();
  return accumulator;
}
 

import it.unimi.dsi.fastutil.ints.Int2DoubleMap.Entry; //导入方法依赖的package包/类
private final Long2DoubleOpenHashMap generateCandidates(final Vector v) {
  final Long2DoubleOpenHashMap accumulator = new Long2DoubleOpenHashMap();
  double l2remscore = 1, // rs4
  rst = 1, squaredQueryPrefixMagnitude = 1;

  for (BidirectionalIterator<Entry> vecIter = v.int2DoubleEntrySet().fastIterator(v.int2DoubleEntrySet().last()); vecIter
      .hasPrevious();) { // iterate over v in reverse order
    final Entry e = vecIter.previous();
    final int dimension = e.getIntKey();
    final double queryWeight = e.getDoubleValue(); // x_j
    final double rscore = l2remscore;
    squaredQueryPrefixMagnitude -= queryWeight * queryWeight;

    L2APPostingList list;
    if ((list = idx.get(dimension)) != null) {
      for (L2APPostingEntry pe : list) {
        numPostingEntries++;
        final long targetID = pe.id(); // y
        if (accumulator.containsKey(targetID) || Double.compare(rscore, theta) >= 0) {
          final double targetWeight = pe.weight(); // y_j
          final double additionalSimilarity = queryWeight * targetWeight; // x_j * y_j
          accumulator.addTo(targetID, additionalSimilarity); // A[y] += x_j * y_j
          final double l2bound = accumulator.get(targetID) + FastMath.sqrt(squaredQueryPrefixMagnitude)
              * pe.magnitude(); // A[y] + ||x'_j|| * ||y'_j||
          if (Double.compare(l2bound, theta) < 0)
            accumulator.remove(targetID); // prune this candidate (early verification)
        }
      }
      rst -= queryWeight * queryWeight; // rs_t -= x_j^2
      l2remscore = FastMath.sqrt(rst); // rs_4 = sqrt(rs_t)
    }
  }
  numCandidates += accumulator.size();
  return accumulator;
}
 

import it.unimi.dsi.fastutil.ints.Int2DoubleMap.Entry; //导入方法依赖的package包/类
private final Vector addToIndex(final Vector v) {
  double bt = 0, b3 = 0, pscore = 0;
  boolean psSaved = false;
  Vector residual = new Vector(v.timestamp());

  for (Entry e : v.int2DoubleEntrySet()) {
    int dimension = e.getIntKey();
    double weight = e.getDoubleValue();

    pscore = b3;
    bt += weight * weight;
    b3 = FastMath.sqrt(bt);

    if (Double.compare(b3, theta) >= 0) {
      if (!psSaved) {
        assert (!ps.containsKey(v.timestamp()));
        ps.put(v.timestamp(), pscore);
        psSaved = true;
      }
      L2APPostingList list;
      if ((list = idx.get(dimension)) == null) {
        list = new L2APPostingList();
        idx.put(dimension, list);
      }
      list.add(v.timestamp(), weight, pscore);
      size++;
    } else {
      residual.put(dimension, weight);
    }
  }
  return residual;
}
 

import it.unimi.dsi.fastutil.ints.Int2DoubleMap.Entry; //导入方法依赖的package包/类
private final Long2DoubleOpenHashMap generateCandidates(final Vector v) {
  final Long2DoubleOpenHashMap accumulator = new Long2DoubleOpenHashMap();
  double remscore = Vector.similarity(v, maxVectorInIndex); // rs3, enhanced remscore bound
  double l2remscore = 1, // rs4
  rst = 1, squaredQueryPrefixMagnitude = 1;

  for (BidirectionalIterator<Entry> vecIter = v.int2DoubleEntrySet().fastIterator(v.int2DoubleEntrySet().last()); vecIter
      .hasPrevious();) { // iterate over v in reverse order
    final Entry e = vecIter.previous();
    final int dimension = e.getIntKey();
    final double queryWeight = e.getDoubleValue(); // x_j
    final double rscore = Math.min(remscore, l2remscore);
    squaredQueryPrefixMagnitude -= queryWeight * queryWeight;

    L2APPostingList list;
    if ((list = idx.get(dimension)) != null) {
      for (L2APPostingEntry pe : list) {
        numPostingEntries++;
        final long targetID = pe.id(); // y
        if (accumulator.containsKey(targetID) || Double.compare(rscore, theta) >= 0) {
          final double targetWeight = pe.weight(); // y_j
          final double additionalSimilarity = queryWeight * targetWeight; // x_j * y_j
          accumulator.addTo(targetID, additionalSimilarity); // A[y] += x_j * y_j
          final double l2bound = accumulator.get(targetID) + FastMath.sqrt(squaredQueryPrefixMagnitude)
              * pe.magnitude(); // A[y] + ||x'_j|| * ||y'_j||
          if (Double.compare(l2bound, theta) < 0)
            accumulator.remove(targetID); // prune this candidate (early verification)
        }
      }
      remscore -= queryWeight * maxVectorInIndex.get(dimension); // rs_3 -= x_j * \hat{c_w}
      rst -= queryWeight * queryWeight; // rs_t -= x_j^2
      l2remscore = FastMath.sqrt(rst); // rs_4 = sqrt(rs_t)
    }
  }
  numCandidates += accumulator.size();
  return accumulator;
}
 

import it.unimi.dsi.fastutil.ints.Int2DoubleMap.Entry; //导入方法依赖的package包/类
private final Vector addToIndex(final Vector v) {
  double b1 = 0, bt = 0, b3 = 0, pscore = 0;
  boolean psSaved = false;
  Vector residual = new Vector(v.timestamp());
  for (Entry e : v.int2DoubleEntrySet()) {
    int dimension = e.getIntKey();
    double weight = e.getDoubleValue();

    pscore = Math.min(b1, b3);
    b1 += weight * maxVectorInWindow.get(dimension);
    bt += weight * weight;
    b3 = FastMath.sqrt(bt);

    if (Double.compare(Math.min(b1, b3), theta) >= 0) {
      if (!psSaved) {
        assert (!ps.containsKey(v.timestamp()));
        ps.put(v.timestamp(), pscore);
        psSaved = true;
      }
      L2APPostingList list;
      if ((list = idx.get(dimension)) == null) {
        list = new L2APPostingList();
        idx.put(dimension, list);
      }
      list.add(v.timestamp(), weight, pscore);
      size++;
    } else {
      residual.put(dimension, weight);
    }
  }
  maxVectorInIndex.updateMaxByDimension(v);
  return residual;
}
 

import it.unimi.dsi.fastutil.ints.Int2DoubleMap.Entry; //导入方法依赖的package包/类
private Long2DoubleOpenHashMap generateCandidates(final Vector v) {
  Long2DoubleOpenHashMap accumulator = new Long2DoubleOpenHashMap();
  double remscore = Vector.similarity(v, maxVector);

  /* candidate generation */
  for (BidirectionalIterator<Entry> it = v.int2DoubleEntrySet().fastIterator(v.int2DoubleEntrySet().last()); it
      .hasPrevious();) { // iterate over v in reverse order
    Entry e = it.previous();
    int dimension = e.getIntKey();
    double queryWeight = e.getDoubleValue(); // x_j

    PostingList list;
    if ((list = idx.get(dimension)) != null) {
      for (PostingEntry pe : list) {
        numPostingEntries++;
        final long targetID = pe.getID(); // y
        if (accumulator.containsKey(targetID) || Double.compare(remscore, theta) >= 0) {
          final double targetWeight = pe.getWeight(); // y_j
          final double additionalSimilarity = queryWeight * targetWeight; // x_j * y_j
          accumulator.addTo(targetID, additionalSimilarity); // A[y] += x_j * y_j
        }
      }
    }
    remscore -= queryWeight * maxVector.get(dimension);
  }
  numCandidates += accumulator.size();
  return accumulator;
}
 

import it.unimi.dsi.fastutil.ints.Int2DoubleMap.Entry; //导入方法依赖的package包/类
private final Vector addToIndex(final Vector v) {
  double bt = 0, b3 = 0, pscore = 0;
  boolean psSaved = false;
  final Vector residual = new Vector(v.timestamp());

  for (Entry e : v.int2DoubleEntrySet()) {
    final int dimension = e.getIntKey();
    final double weight = e.getDoubleValue();
    pscore = b3;
    bt += weight * weight;
    b3 = FastMath.sqrt(bt);

    if (Double.compare(b3, theta) >= 0) { // bound larger than threshold, start indexing
      if (!psSaved) {
        ps.put(v.timestamp(), pscore);
        psSaved = true;
      }
      StreamingL2APPostingList list;
      if ((list = idx.get(dimension)) == null) {
        list = new StreamingL2APPostingList();
        idx.put(dimension, list);
      }
      list.add(v.timestamp(), weight, pscore);
      size++;
    } else {
      residual.put(dimension, weight);
    }
  }
  return residual;
}
 

import it.unimi.dsi.fastutil.ints.Int2DoubleMap.Entry; //导入方法依赖的package包/类
private final Vector addToIndex(final Vector v) {
  double b1 = 0, bt = 0, b3 = 0, pscore = 0;
  boolean psSaved = false;
  final Vector residual = new Vector(v.timestamp());

  for (Entry e : v.int2DoubleEntrySet()) {
    final int dimension = e.getIntKey();
    final double weight = e.getDoubleValue();
    pscore = Math.min(b1, b3);
    b1 += weight * maxVector.get(dimension);
    bt += weight * weight;
    b3 = FastMath.sqrt(bt);

    if (Double.compare(Math.min(b1, b3), theta) >= 0) { // bound larger than threshold, start indexing
      if (!psSaved) {
        ps.put(v.timestamp(), pscore);
        psSaved = true;
      }
      StreamingL2APPostingList list;
      if ((list = idx.get(dimension)) == null) {
        list = new StreamingL2APPostingList();
        idx.put(dimension, list);
      }
      list.add(v.timestamp(), weight, pscore);
      size++;
    } else {
      residual.put(dimension, weight);
    }
  }
  return residual;
}
 

import it.unimi.dsi.fastutil.ints.Int2DoubleMap.Entry; //导入方法依赖的package包/类
private final void generateCandidates(final Vector v) {
  double l2remscore = 1, // rs4
  rst = 1, squaredQueryPrefixMagnitude = 1;

  for (BidirectionalIterator<Entry> vecIter = v.int2DoubleEntrySet().fastIterator(v.int2DoubleEntrySet().last()); vecIter
      .hasPrevious();) { // iterate over v in reverse order
    final Entry e = vecIter.previous();
    final int dimension = e.getIntKey();
    final double queryWeight = e.getDoubleValue(); // x_j
    final double rscore = l2remscore;
    squaredQueryPrefixMagnitude -= queryWeight * queryWeight;

    StreamingL2APPostingList list;
    if ((list = idx.get(dimension)) != null) {
      for (StreamingL2APPostingListIterator listIter = list.reverseIterator(); listIter.hasPrevious();) {
        final L2APPostingEntry pe = listIter.previous();
        final long targetID = pe.id(); // y

        // time filtering
        final long deltaT = v.timestamp() - targetID;
        if (Doubles.compare(deltaT, tau) > 0) {
          listIter.next(); // back off one position
          size -= listIter.nextIndex(); // update index size before cutting
          listIter.cutHead(); // prune the head
          break;
        }
        numPostingEntries++;

        final double ff = forgettingFactor(lambda, deltaT);
        // forgetting factor applied directly to the l2prefix bound
        if (accumulator.containsKey(targetID) || Double.compare(rscore * ff, theta) >= 0) {
          final double targetWeight = pe.weight(); // y_j
          final double additionalSimilarity = queryWeight * targetWeight; // x_j * y_j
          accumulator.addTo(targetID, additionalSimilarity); // A[y] += x_j * y_j
          final double l2bound = accumulator.get(targetID) + FastMath.sqrt(squaredQueryPrefixMagnitude)
              * pe.magnitude(); // A[y] + ||x'_j|| * ||y'_j||
          // forgetting factor applied directly to the l2sum bound
          if (Double.compare(l2bound * ff, theta) < 0) {
            accumulator.remove(targetID); // prune this candidate (early verification)
          }
        }
      }
      rst -= queryWeight * queryWeight; // rs_t -= x_j^2
      l2remscore = FastMath.sqrt(rst); // rs_4 = sqrt(rs_t)
    }
  }
  numCandidates += accumulator.size();
}
 

import it.unimi.dsi.fastutil.ints.Int2DoubleMap.Entry; //导入方法依赖的package包/类
private final void generateCandidates(final Vector v) {
  double remscore = maxVectorInIndex.simimarityFF(v); // rs3, enhanced remscore bound with addded forgetting factor per dimension
  double l2remscore = 1, // rs4
  rst = 1, squaredQueryPrefixMagnitude = 1;

  for (BidirectionalIterator<Entry> vecIter = v.int2DoubleEntrySet().fastIterator(v.int2DoubleEntrySet().last()); vecIter
      .hasPrevious();) { // iterate over v in reverse order
    final Entry e = vecIter.previous();
    final int dimension = e.getIntKey();
    final double queryWeight = e.getDoubleValue(); // x_j
    squaredQueryPrefixMagnitude -= queryWeight * queryWeight;

    StreamingL2APPostingList list;
    if ((list = idx.get(dimension)) != null) {
      for (StreamingL2APPostingListIterator listIter = list.iterator(); listIter.hasNext();) {
        final L2APPostingEntry pe = listIter.next();
        final long targetID = pe.id(); // y

        // time filtering
        numPostingEntries++;
        final long deltaT = v.timestamp() - targetID;
        if (Doubles.compare(deltaT, tau) > 0) {
          listIter.remove();
          size--;
          continue;
        }

        final double ff = forgettingFactor(lambda, deltaT);
        // forgetting factor applied directly to the l2prefix bounds
        final double rscore = Math.min(remscore, l2remscore * ff);
        if (accumulator.containsKey(targetID) || Double.compare(rscore, theta) >= 0) {
          final double targetWeight = pe.weight(); // y_j
          final double additionalSimilarity = queryWeight * targetWeight; // x_j * y_j
          accumulator.addTo(targetID, additionalSimilarity); // A[y] += x_j * y_j
          final double l2bound = accumulator.get(targetID) + FastMath.sqrt(squaredQueryPrefixMagnitude)
              * pe.magnitude(); // A[y] + ||x'_j|| * ||y'_j||
          // forgetting factor applied directly to the l2bound
          if (Double.compare(l2bound * ff, theta) < 0)
            accumulator.remove(targetID); // prune this candidate (early verification)
        }
      }
      final double dimFF = maxVectorInIndex.dimensionFF(dimension, v.timestamp());
      remscore -= queryWeight * maxVectorInIndex.get(dimension) * dimFF; // rs_3 -= x_j * \hat{c_w}
      rst -= queryWeight * queryWeight; // rs_t -= x_j^2
      l2remscore = FastMath.sqrt(rst); // rs_4 = sqrt(rs_t)
    }
  }
  numCandidates += accumulator.size();
}