List of iterateNonZero() Examples

Examples of iterateNonZero()

de.jungblut.math.DoubleVector.iterateNonZero()
org.apache.mahout.math.DenseVector.iterateNonZero()
Returns an iterator that traverses this Vector from 0 to cardinality-1, in that order. @see Iterable#iterator
org.apache.mahout.math.RandomAccessSparseVector.iterateNonZero()
NOTE: this implementation reuses the Vector.Element instance for each call of next(). If you need to preserve the instance, you need to make a copy of it @return an {@link NonZeroIterator} over the Elements. @see #getElement(int)
org.apache.mahout.math.SequentialAccessSparseVector.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
org.apache.mahout.matrix.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 java.util.Iterator} over all non-zero elements

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

        // classification yields probabilities
        Vector probabilities = classifier.classify(vector);
        // policy selects weights for models given those probabilities
        Vector weights = policy.select(probabilities);
        // training causes all models to observe data
        for (Iterator<Vector.Element> it = weights.iterateNonZero(); it
            .hasNext();) {
          int index = it.next().index();
          classifier.train(index, vector, weights.get(index));
        }
      }

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

        // classification yields probabilities
        Vector probabilities = classifier.classify(vector);
        // policy selects weights for models given those probabilities
        Vector weights = policy.select(probabilities);
        // training causes all models to observe data
        for (Iterator<Vector.Element> it = weights.iterateNonZero(); it
            .hasNext();) {
          int index = it.next().index();
          classifier.train(index, vector, weights.get(index));
        }
      }

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

         "This is probably because the --numWords argument is set too small.  \n"
         + "\tIt needs to be >= than the number of words (terms actually) in the corpus and can be \n"
         + "\tlarger if some storage inefficiency can be tolerated.", e1);
      }


      for (Iterator<Vector.Element> iter = wordCounts.iterateNonZero(); iter.hasNext();) {
        Vector.Element e = iter.next();
        int w = e.index();


        for (int k = 0; k < state.getNumTopics(); ++k) {
          double vwUpdate = doc.phi(k, w) + Math.log(e.get());

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

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

    @Override
    protected void map(IntWritable userOrItemID, VectorWritable ratingsWritable, Context ctx)
        throws IOException, InterruptedException {
      Vector ratings = new SequentialAccessSparseVector(ratingsWritable.get());
      List<Vector> featureVectors = Lists.newArrayList();
      Iterator<Vector.Element> interactions = ratings.iterateNonZero();
      while (interactions.hasNext()) {
        int index = interactions.next().index();
        featureVectors.add(UorM.get(index));
      }

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

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

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

  protected void map(IntWritable key, VectorWritable value, Context context)
      throws IOException, InterruptedException {
    Vector vector = value.get();
    int label = key.get();
    double sigmaK = labelSum.get(label);
    Iterator<Element> it = vector.iterateNonZero();
    while (it.hasNext()) {
      Element e = it.next();
      double numerator = featureSum.get(e.index()) - e.get() + alphaI;
      double denominator = totalSum - sigmaK + vocabCount;
      double weight = Math.log(numerator / denominator);

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

  protected void update(double label, Vector dataPoint, LinearModel model) {
    if (label > 0) {
      // case one
      Vector updateVector = dataPoint.times(1 / this.promotionStep);
      log.info("Winnow update positive: {}", updateVector);
      Iterator<Vector.Element> iter = updateVector.iterateNonZero();
      while (iter.hasNext()) {
        Vector.Element element = iter.next();
        model.timesDelta(element.index(), element.get());
      }
    } else {

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

      }
    } 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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