Package org.apache.mahout.math

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

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      }
    } else {
      // case two
      Vector updateVector = dataPoint.times(1 / this.promotionStep);
      log.info("Winnow update negative: {}", updateVector);
      for (Vector.Element element : updateVector.nonZeroes()) {
        if (element.get() != 0) {
          model.timesDelta(element.index(), element.get());
        }
      }
    }
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    if (usersToRecommendFor != null && !usersToRecommendFor.contains(userID)) {
      return;
    }
    Vector userVector = maybePruneUserVector(value.get());

    for (Element e : userVector.nonZeroes()) {
      itemIndexWritable.set(e.index());
      vectorOrPref.set(userID, (float) e.get());
      context.write(itemIndexWritable, vectorOrPref);
    }
  }
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    Vector ratings = ratingsWritable.get();
    int userIndex = userIndexWritable.get();
    final OpenIntHashSet alreadyRatedItems = new OpenIntHashSet(ratings.getNumNondefaultElements());

    for (Vector.Element e : ratings.nonZeroes()) {
      alreadyRatedItems.add(e.index());
    }

    final TopItemsQueue topItemsQueue = new TopItemsQueue(recommendationsPerUser);
    final Vector userFeatures = U.get(userIndex);
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  public static Vector solveExplicit(VectorWritable ratingsWritable, OpenIntObjectHashMap<Vector> uOrM,
    double lambda, int numFeatures) {
    Vector ratings = ratingsWritable.get();

    List<Vector> featureVectors = Lists.newArrayListWithCapacity(ratings.getNumNondefaultElements());
    for (Vector.Element e : ratings.nonZeroes()) {
      int index = e.index();
      featureVectors.add(uOrM.get(index));
    }

    return AlternatingLeastSquaresSolver.solve(featureVectors, ratings, lambda, numFeatures);
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    // check if sizeOFNonZeroElementsInVector = maxEntries
    assertEquals(6, VectorHelper.topEntries(v, 6).size());
    // check if sizeOfNonZeroElementsInVector < maxEntries
    assertTrue(VectorHelper.topEntries(v, 9).size() < 9);
    // check if sizeOfNonZeroElementsInVector > maxEntries
    assertTrue(VectorHelper.topEntries(v, 5).size() < Iterables.size(v.nonZeroes()));
  }

  @Test
  public void testTopEntriesWhenAllZeros() throws Exception {
    Vector v = new SequentialAccessSparseVector(10);
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    for (int i = 0; i < numHashFunctions; i++) {
      minHashValues[i] = Integer.MAX_VALUE;
    }

    for (int i = 0; i < numHashFunctions; i++) {
      for (Vector.Element ele : featureVector.nonZeroes()) {
        int value = hashValue ? (int) ele.get() : ele.index();
        bytesToHash[0] = (byte) (value >> 24);
        bytesToHash[1] = (byte) (value >> 16);
        bytesToHash[2] = (byte) (value >> 8);
        bytesToHash[3] = (byte) value;
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        // 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 (Vector.Element e : weights.nonZeroes()) {
          int index = e.index();
          classifier.train(index, vector, weights.get(index));
        }
      }
      // compute the posterior models
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        // classification yields probabilities
        Vector probabilities = classifier.classify(vector);
        // policy selects weights for models given those probabilities
        Vector weights = classifier.getPolicy().select(probabilities);
        // training causes all models to observe data
        for (Vector.Element e : weights.nonZeroes()) {
          int index = e.index();
          classifier.train(index, vector, weights.get(index));
        }
      }
      // compute the posterior models
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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 (Vector.Element vecEl : vec.nonZeroes()) {
          int i = vecEl.index();
          extendAColIfNeeded(i, aRowCount + 1);
          aCols[i].setQuick(aRowCount, vecEl.get());
        }
      }
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              /* 100% zero A column in the block, skip it as sparse */
              continue;
            }
            int j = -1;
            for (Vector.Element aEl : aCol.nonZeroes()) {
              j = aEl.index();

              /*
               * now we compute only swathes between aRowBegin..aRowBegin+bh
               * exclusive. it seems like a deficiency but in fact i think it
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