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

      }
    } else {
      // case two
      Vector updateVector = dataPoint.times(1 / this.promotionStep);
      log.info("Winnow update negative: {}", updateVector);
      Iterator<Vector.Element> iter = updateVector.iterateNonZero();
      while (iter.hasNext()) {
        Vector.Element element = iter.next();
        model.timesDelta(element.index(), element.get());
      }
    }

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      predictionVector = predictionVector == null
          ? prefAndSimilarityColumn.getSimilarityColumn()
          : predictionVector.plus(prefAndSimilarityColumn.getSimilarityColumn());
    }


    Iterator<Vector.Element> predictions = predictionVector.iterateNonZero();
    List<RecommendedItem> recommendations = new ArrayList<RecommendedItem>();
    while (predictions.hasNext() && recommendations.size() < recommendationsPerUser) {
      Vector.Element prediction = predictions.next();
      /* NaN means the user already knows this item */
      if (!Double.isNaN(prediction.get())) {

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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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    }


    Queue<RecommendedItem> topItems = new PriorityQueue<RecommendedItem>(recommendationsPerUser + 1,
    Collections.reverseOrder(ByValueRecommendedItemComparator.getInstance()));


    Iterator<Vector.Element> recommendationVectorIterator = recommendationVector.iterateNonZero();
    while (recommendationVectorIterator.hasNext()) {
      Vector.Element element = recommendationVectorIterator.next();
      int index = element.index();


      long itemID = indexItemIDMap.get(index);

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        throws IOException, InterruptedException {


      Vector v = vectorWritable.get();
      double weight = similarity.weight(v);


      Iterator<Vector.Element> elementsIterator = v.iterateNonZero();
      while (elementsIterator.hasNext()) {
        Vector.Element element = elementsIterator.next();
        int column = element.index();
        double value = element.get();
        ctx.write(new VarIntWritable(column), new WeightedOccurrence(row.get(), value, weight));

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      Vector rowVector = similarity.normalize(vectorWritable.get());


      int numNonZeroEntries = 0;
      double maxValue = Double.MIN_VALUE;


      Iterator<Vector.Element> nonZeroElements = rowVector.iterateNonZero();
      while (nonZeroElements.hasNext()) {
        Vector.Element element = nonZeroElements.next();
        RandomAccessSparseVector partialColumnVector = new RandomAccessSparseVector(Integer.MAX_VALUE);
        partialColumnVector.setQuick(row.get(), element.get());
        ctx.write(new IntWritable(element.index()), new VectorWritable(partialColumnVector));

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        throws IOException, InterruptedException {
      Iterator<VectorWritable> partialDotsIterator = partialDots.iterator();
      Vector dots = partialDotsIterator.next().get();
      while (partialDotsIterator.hasNext()) {
        Vector toAdd = partialDotsIterator.next().get();
        Iterator<Vector.Element> nonZeroElements = toAdd.iterateNonZero();
        while (nonZeroElements.hasNext()) {
          Vector.Element nonZeroElement = nonZeroElements.next();
          dots.setQuick(nonZeroElement.index(), dots.getQuick(nonZeroElement.index()) + nonZeroElement.get());
        }
      }

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    @Override
    protected void map(IntWritable row, VectorWritable similaritiesWritable, Context ctx)
        throws IOException, InterruptedException {
      Vector similarities = similaritiesWritable.get();
      TopK<Vector.Element> topKQueue = new TopK<Vector.Element>(maxSimilaritiesPerRow, Vectors.BY_VALUE);
      Iterator<Vector.Element> nonZeroElements = similarities.iterateNonZero();
      while (nonZeroElements.hasNext()) {
        Vector.Element nonZeroElement = nonZeroElements.next();
        topKQueue.offer(new Vectors.TemporaryElement(nonZeroElement));
        Vector transposedPartial = similarities.like();
        transposedPartial.setQuick(row.get(), nonZeroElement.get());

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      throws IOException, InterruptedException {


      Vector v = vectorWritable.get();
      double weight = similarity.weight(v);


      Iterator<Vector.Element> elementsIterator = v.iterateNonZero();
      while (elementsIterator.hasNext()) {
        Vector.Element element = elementsIterator.next();
        int column = element.index();
        double value = element.get();
        ctx.write(new VarIntWritable(column), new WeightedOccurrence(row.get(), value, weight));

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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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