Examples of org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator

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

Examples of org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator

org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator
Adds notion of iterating over long primitives in the style of an {@link Iterator} -- asopposed to iterating over {@link Long}. Implementations of this interface however also implement {@link Iterator} and {@link Iterable} over {@link Long} for convenience.

  }
  
  private void updateAllRecommendableItems() {
    for (Map.Entry<Long,FastByIDMap<RunningAverage>> entry : averageDiffs.entrySet()) {
      allRecommendableItemIDs.add(entry.getKey());
      LongPrimitiveIterator it = entry.getValue().keySetIterator();
      while (it.hasNext()) {
        allRecommendableItemIDs.add(it.next());
      }
    }
    allRecommendableItemIDs.rehash();
  }

View Full Code Here

    
    int numUsers = dataModel.getNumUsers();
    userMap = new FastByIDMap<Integer>(numUsers);
    
    int idx = 0;
    LongPrimitiveIterator userIterator = dataModel.getUserIDs();
    while (userIterator.hasNext()) {
      userMap.put(userIterator.nextLong(), idx++);
    }
    
    int numItems = dataModel.getNumItems();
    itemMap = new FastByIDMap<Integer>(numItems);
    
    idx = 0;
    LongPrimitiveIterator itemIterator = dataModel.getItemIDs();
    while (itemIterator.hasNext()) {
      itemMap.put(itemIterator.nextLong(), idx++);
    }
    
    double average = getAveragePreference();
    double defaultValue = Math.sqrt((average - 1.0) / numFeatures);

View Full Code Here

  }
  
  private void recachePreferences() throws TasteException {
    cachedPreferences.clear();
    DataModel dataModel = getDataModel();
    LongPrimitiveIterator it = dataModel.getUserIDs();
    while (it.hasNext()) {
      for (Preference pref : dataModel.getPreferencesFromUser(it.nextLong())) {
        cachedPreferences.add(pref);
      }
    }
  }

View Full Code Here

  }
  
  private double getAveragePreference() throws TasteException {
    RunningAverage average = new FullRunningAverage();
    DataModel dataModel = getDataModel();
    LongPrimitiveIterator it = dataModel.getUserIDs();
    while (it.hasNext()) {
      for (Preference pref : dataModel.getPreferencesFromUser(it.nextLong())) {
        average.addDatum(pref.getValue());
      }
    }
    return average.getAverage();
  }

View Full Code Here

  }
  
  private void recompute() throws TasteException {
    Counters itemPreferenceCounts = new Counters();
    int numUsers = 0;
    LongPrimitiveIterator it = dataModel.getUserIDs();
    while (it.hasNext()) {
      PreferenceArray prefs = dataModel.getPreferencesFromUser(it.nextLong());
      int size = prefs.length();
      for (int i = 0; i < size; i++) {
        itemPreferenceCounts.increment(prefs.getItemID(i));
      }
      numUsers++;

View Full Code Here

    DataModel model = getDataModel();
    int numUsers = model.getNumUsers();
    if (numUsers > 0) {
      List<FastIDSet> newClusters = new ArrayList<FastIDSet>(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);
      }

View Full Code Here

  private FastByIDMap<List<RecommendedItem>> computeTopRecsPerUserID(Iterable<FastIDSet> clusters)
    throws TasteException {
    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.next(), recs);
      }
    }
    return recsPerUser;
  }

View Full Code Here

  }
  
  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.next()));
    }
    
    TopItems.Estimator<Long> estimator = new Estimator(cluster);
    
    List<RecommendedItem> topItems =

View Full Code Here

  }
  
  private static FastByIDMap<FastIDSet> computeClustersPerUserID(Collection<FastIDSet> clusters) {
    FastByIDMap<FastIDSet> clustersPerUser = new FastByIDMap<FastIDSet>(clusters.size());
    for (FastIDSet cluster : clusters) {
      LongPrimitiveIterator it = cluster.iterator();
      while (it.hasNext()) {
        clustersPerUser.put(it.next(), cluster);
      }
    }
    return clustersPerUser;
  }

View Full Code Here

    
    @Override
    public double estimate(Long itemID) throws TasteException {
      DataModel dataModel = getDataModel();
      RunningAverage average = new FullRunningAverage();
      LongPrimitiveIterator it = cluster.iterator();
      while (it.hasNext()) {
        Float pref = dataModel.getPreferenceValue(it.next(), itemID);
        if (pref != null) {
          average.addDatum(pref);
        }
      }
      return average.getAverage();

View Full Code Here

0 1 2 3 4 5 6 7 8 9

TOP

Related Classes of org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator

mia.recommender.ch02.ItemBaseRecommender

mia.recommender.ch02.UserBaseRecommender

net.myrrix.client.CLI

net.myrrix.client.translating.TranslatingClientRecommender

net.myrrix.common.collection.FastIDSetTest

net.myrrix.common.math.MatrixUtils

net.myrrix.common.random.RandomUtils

net.myrrix.online.factorizer.als.AlternatingLeastSquares

net.myrrix.online.generation.GenerationLoader

net.myrrix.online.generation.GenerationSerializer

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.