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

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

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  private void addSomeOf(FastIDSet possibleItemIDs, FastIDSet itemIDs) {
    if (itemIDs.size() > maxItemsPerUser) {
      LongPrimitiveIterator it =
          new SamplingLongPrimitiveIterator(itemIDs.iterator(), (double) maxItemsPerUser / itemIDs.size());
      while (it.hasNext()) {
        possibleItemIDs.add(it.nextLong());
      }
    } else {
      possibleItemIDs.addAll(itemIDs);
    }
  }
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    double greatestSimilarity = Double.NEGATIVE_INFINITY;
    while (someUsers.hasNext()) {
      long userID1 = someUsers.next();
      LongPrimitiveIterator it2 = cluster2.iterator();
      while (it2.hasNext()) {
        double theSimilarity = similarity.userSimilarity(userID1, it2.nextLong());
        if (theSimilarity > greatestSimilarity) {
          greatestSimilarity = theSimilarity;
        }
      }
    }
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  @Override
  protected FastIDSet doGetCandidateItems(long[] preferredItemIDs, DataModel dataModel) throws TasteException {
    FastIDSet possibleItemIDs = new FastIDSet(dataModel.getNumItems());
    LongPrimitiveIterator allItemIDs = dataModel.getItemIDs();
    while (allItemIDs.hasNext()) {
      possibleItemIDs.add(allItemIDs.nextLong());
    }
    possibleItemIDs.removeAll(preferredItemIDs);
    return possibleItemIDs;
  }
}
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  @Override
  public long[] allSimilarItemIDs(long itemID) {
    FastIDSet allSimilarItemIDs = new FastIDSet();
    LongPrimitiveIterator allItemIDs = trackData.keySetIterator();
    while (allItemIDs.hasNext()) {
      long possiblySimilarItemID = allItemIDs.nextLong();
      if (!Double.isNaN(itemSimilarity(itemID, possiblySimilarItemID))) {
        allSimilarItemIDs.add(possiblySimilarItemID);
      }
    }
    return allSimilarItemIDs.toArray();
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   */
  public static FastByIDMap<PreferenceArray> toDataMap(DataModel dataModel) throws TasteException {
    FastByIDMap<PreferenceArray> data = new FastByIDMap<PreferenceArray>(dataModel.getNumUsers());
    LongPrimitiveIterator it = dataModel.getUserIDs();
    while (it.hasNext()) {
      long userID = it.nextLong();
      data.put(userID, dataModel.getPreferencesFromUser(userID));
    }
    return data;
  }
 
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  double getAveragePreference() throws TasteException {
    RunningAverage average = new FullRunningAverage();
    LongPrimitiveIterator userIDs = dataModel.getUserIDs();
    while (userIDs.hasNext()) {
      for (Preference preference : dataModel.getPreferencesFromUser(userIDs.nextLong())) {
        average.addDatum(preference.getValue());
      }
    }
    return average.getAverage();
  }
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  private void cachePreferences() throws TasteException {
    cachedPreferences.clear();
    LongPrimitiveIterator userIDs = dataModel.getUserIDs();
    while (userIDs.hasNext()) {
      for (Preference pref : dataModel.getPreferencesFromUser(userIDs.nextLong())) {
        cachedPreferences.add(new SVDPreference(pref.getUserID(), pref.getItemID(), pref.getValue(), 0.0));
      }
    }
  }
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      List<FastIDSet> newClusters = Lists.newArrayListWithCapacity(numUsers);
      // Begin with a cluster for each user:
      LongPrimitiveIterator it = model.getUserIDs();
      while (it.hasNext()) {
        FastIDSet newCluster = new FastIDSet();
        newCluster.add(it.nextLong());
        newClusters.add(newCluster);
      }
      if (numUsers > 1) {
        findClusters(newClusters);
      }
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    FastByIDMap<List<RecommendedItem>> recsPerUser = new FastByIDMap<List<RecommendedItem>>();
    for (FastIDSet cluster : clusters) {
      List<RecommendedItem> recs = computeTopRecsForCluster(cluster);
      LongPrimitiveIterator it = cluster.iterator();
      while (it.hasNext()) {
        recsPerUser.put(it.nextLong(), recs);
      }
    }
    return recsPerUser;
  }
 
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  private List<RecommendedItem> computeTopRecsForCluster(FastIDSet cluster) throws TasteException {
    DataModel dataModel = getDataModel();
    FastIDSet possibleItemIDs = new FastIDSet();
    LongPrimitiveIterator it = cluster.iterator();
    while (it.hasNext()) {
      possibleItemIDs.addAll(dataModel.getItemIDsFromUser(it.nextLong()));
    }
   
    TopItems.Estimator<Long> estimator = new Estimator(cluster);
   
    List<RecommendedItem> topItems =
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