Package org.apache.mahout.cf.taste.impl.common

Examples of org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator.skip()

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    DataModel dataModel = getDataModel();
    int numItems = dataModel.getNumItems();
    List<RecommendedItem> result = new ArrayList<RecommendedItem>(howMany);
    while (result.size() < howMany) {
      LongPrimitiveIterator it = dataModel.getItemIDs();
      it.skip(random.nextInt(numItems));
      long itemID = it.next();
      if (dataModel.getPreferenceValue(userID, itemID) == null) {
        result.add(new GenericRecommendedItem(itemID, randomPref()));
      }
    }
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    DataModel dataModel = getDataModel();
    int numItems = dataModel.getNumItems();
    List<RecommendedItem> result = Lists.newArrayListWithCapacity(howMany);
    while (result.size() < howMany) {
      LongPrimitiveIterator it = dataModel.getItemIDs();
      it.skip(random.nextInt(numItems));
      long itemID = it.next();
      if (dataModel.getPreferenceValue(userID, itemID) == null) {
        result.add(new GenericRecommendedItem(itemID, randomPref()));
      }
    }
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    DataModel dataModel = getDataModel();
    int numItems = dataModel.getNumItems();
    List<RecommendedItem> result = new ArrayList<RecommendedItem>(howMany);
    while (result.size() < howMany) {
      LongPrimitiveIterator it = dataModel.getItemIDs();
      it.skip(random.nextInt(numItems));
      long itemID = it.next();
      if (dataModel.getPreferenceValue(userID, itemID) == null) {
        result.add(new GenericRecommendedItem(itemID, randomPref()));
      }
    }
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   */
  public static int randomFrom(FastIDSet set, RandomGenerator random) {
    int size = set.size();
    Preconditions.checkArgument(size > 0, "Empty set");
    LongPrimitiveIterator it = set.iterator();
    it.skip(random.nextInt(size));
    return (int) it.nextLong();
  }

  /**
   * @param n approximate number of items to choose
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    LongPrimitiveIterator keyIterator = vectors.keySetIterator();
    Node[][] map = new Node[mapSize][mapSize];
    for (Node[] mapRow : map) {
      for (int j = 0; j < mapSize; j++) {
        if (pascalDistribution != null) {
          keyIterator.skip(pascalDistribution.sample());
        }
        while (!keyIterator.hasNext()) {
          keyIterator = vectors.keySetIterator(); // Start over, a little imprecise but affects it not much
          Preconditions.checkState(keyIterator.hasNext());
          if (pascalDistribution != null) {
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        }
        while (!keyIterator.hasNext()) {
          keyIterator = vectors.keySetIterator(); // Start over, a little imprecise but affects it not much
          Preconditions.checkState(keyIterator.hasNext());
          if (pascalDistribution != null) {
            keyIterator.skip(pascalDistribution.sample());
          }
        }
        float[] sampledVector = vectors.get(keyIterator.nextLong());
        mapRow[j] = new Node(sampledVector);
      }
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    DataModel dataModel = getDataModel();
    int numItems = dataModel.getNumItems();
    List<RecommendedItem> result = Lists.newArrayListWithCapacity(howMany);
    while (result.size() < howMany) {
      LongPrimitiveIterator it = dataModel.getItemIDs();
      it.skip(random.nextInt(numItems));
      long itemID = it.next();
      if (includeKnownItems || dataModel.getPreferenceValue(userID, itemID) == null) {
        result.add(new GenericRecommendedItem(itemID, randomPref()));
      }
    }
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