Examples of org.apache.mahout.math.Vector.iterateNonZero()

org.apache.mahout.math.Vector.iterateNonZero()
Iterates over all non-zero elements.
NOTE: Implementations may choose to reuse the Element returned for performance reasons, so if you need a copy of it, you should call {@link #getElement} for the given index @return An {@link Iterator} over all non-zero elements

    Iterator<VectorWritable> it = values.iterator();
    if (!it.hasNext()) {
      return;
    }
    Vector value = it.next().get();
    Iterator<Vector.Element> it1 = value.iterateNonZero();
    Vector vector = new RandomAccessSparseVector((int) featureCount, value.getNumNondefaultElements());
    while (it1.hasNext()) {
      Vector.Element e = it1.next();
      if (!dictionary.containsKey(e.index())) {
        continue;

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        for (int i = 0; i < vecSize; i++) {
          extendAColIfNeeded(i, aRowCount + 1);
          aCols[i].setQuick(aRowCount, vec.getQuick(i));
        }
      } else if (vec.size() > 0) {
        for (Iterator<Vector.Element> vecIter = vec.iterateNonZero(); vecIter.hasNext();) {
          Vector.Element vecEl = vecIter.next();
          int i = vecEl.index();
          extendAColIfNeeded(i, aRowCount + 1);
          aCols[i].setQuick(aRowCount, vecEl.get());
        }

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        for (int i = 0; i < vecSize; i++) {
          extendAColIfNeeded(i, aRowCount + 1);
          aCols[i].setQuick(aRowCount, vec.getQuick(i));
        }
      } else {
        for (Iterator<Vector.Element> vecIter = vec.iterateNonZero(); vecIter.hasNext();) {
          Vector.Element vecEl = vecIter.next();
          int i = vecEl.index();
          extendAColIfNeeded(i, aRowCount + 1);
          aCols[i].setQuick(aRowCount, vecEl.get());
        }

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          Vector aCol;
          if (btIndex > aCols.length || (aCol = aCols[btIndex]) == null) {
            continue;
          }
          int j = -1;
          for (Iterator<Vector.Element> aColIter = aCol.iterateNonZero(); aColIter.hasNext();) {
            Vector.Element aEl = aColIter.next();
            j = aEl.index();


            // outKey.setTaskItemOrdinal(j);
            // outValue.set(btVec.times(aEl.get())); // assign might work better

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              /* 100% zero A column in the block, skip it as sparse */
              continue;
            }
            int j = -1;
            for (Iterator<Vector.Element> aColIter = aCol.iterateNonZero(); aColIter.hasNext();) {
              Vector.Element aEl = aColIter.next();
              j = aEl.index();


              /*
               * now we compute only swathes between aRowBegin..aRowBegin+bh

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      boolean firstIsOutFrag =  ((VectorWritable)v.get(0)).get().size() == outCardinality;
      Vector outFrag = firstIsOutFrag ? ((VectorWritable)v.get(0)).get() : ((VectorWritable)v.get(1)).get();
      Vector multiplier = firstIsOutFrag ? ((VectorWritable)v.get(1)).get() : ((VectorWritable)v.get(0)).get();


      VectorWritable outVector = new VectorWritable();
      Iterator<Vector.Element> it = multiplier.iterateNonZero();
      while (it.hasNext()) {
        Vector.Element e = it.next();
        row.set(e.index());
        outVector.set(outFrag.times(e.get()));
        out.collect(row, outVector);

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    int column = TasteHadoopUtils.idToIndex(rowIndex.get());
    VectorWritable itemVector = new VectorWritable(new RandomAccessSparseVector(Integer.MAX_VALUE, 1));
    itemVector.setWritesLaxPrecision(true);


    Iterator<Vector.Element> iterator = userRatings.iterateNonZero();
    while (iterator.hasNext()) {
      Vector.Element elem = iterator.next();
      itemVector.get().setQuick(column, elem.get());
      ctx.write(new IntWritable(elem.index()), itemVector);
    }

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    for (PrefAndSimilarityColumnWritable prefAndSimilarityColumn : values) {
      Vector simColumn = prefAndSimilarityColumn.getSimilarityColumn();
      float prefValue = prefAndSimilarityColumn.getPrefValue();
      /* count the number of items used for each prediction */
      Iterator<Vector.Element> usedItemsIterator = simColumn.iterateNonZero();
      while (usedItemsIterator.hasNext()) {
        int itemIDIndex = usedItemsIterator.next().index();
        numberOfSimilarItemsUsed.setQuick(itemIDIndex, numberOfSimilarItemsUsed.getQuick(itemIDIndex) + 1);
      }

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    long userID = key.get();
    if (usersToRecommendFor != null && !usersToRecommendFor.contains(userID)) {
      return;
    }
    Vector userVector = maybePruneUserVector(value.get());
    Iterator<Vector.Element> it = userVector.iterateNonZero();
    VarIntWritable itemIndexWritable = new VarIntWritable();
    VectorOrPrefWritable vectorOrPref = new VectorOrPrefWritable();
    while (it.hasNext()) {
      Vector.Element e = it.next();
      itemIndexWritable.set(e.index());

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      Vector ratings = ratingsWritable.get();
      final int userID = userIDWritable.get();
      final OpenIntHashSet alreadyRatedItems = new OpenIntHashSet(ratings.getNumNondefaultElements());
      final TopK<RecommendedItem> topKItems = new TopK<RecommendedItem>(recommendationsPerUser, BY_PREFERENCE_VALUE);


      Iterator<Vector.Element> ratingsIterator = ratings.iterateNonZero();
      while (ratingsIterator.hasNext()) {
        alreadyRatedItems.add(ratingsIterator.next().index());
      }


      M.forEachPair(new IntObjectProcedure<Vector>() {

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