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

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

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  @Override
  public double getSimilarity(FastIDSet cluster1, FastIDSet cluster2) throws TasteException {
    if (cluster1.isEmpty() || cluster2.isEmpty()) {
      return Double.NaN;
    }
    LongPrimitiveIterator someUsers = SamplingLongPrimitiveIterator.maybeWrapIterator(cluster1.iterator(),
      samplingRate);
    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.next());
        if (theSimilarity > greatestSimilarity) {
          greatestSimilarity = theSimilarity;
        }
      }
    }
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  private void buildAverageDiffs() throws TasteException {
    try {
      buildAveragesLock.writeLock().lock();
      DataModel dataModel = getDataModel();
      LongPrimitiveIterator it = dataModel.getUserIDs();
      while (it.hasNext()) {
        long userID = it.nextLong();
        PreferenceArray prefs = dataModel.getPreferencesFromUser(userID);
        int size = prefs.length();
        for (int i = 0; i < size; i++) {
          long itemID = prefs.getItemID(i);
          float value = prefs.getValue(i);
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  public double getSimilarity(FastIDSet cluster1, FastIDSet cluster2) throws TasteException {
    if (cluster1.isEmpty() || cluster2.isEmpty()) {
      return Double.NaN;
    }
    double leastSimilarity = Double.POSITIVE_INFINITY;
    LongPrimitiveIterator someUsers = SamplingLongPrimitiveIterator.maybeWrapIterator(cluster1.iterator(),
      samplingRate);
    while (someUsers.hasNext()) {
      long userID1 = someUsers.next();
      LongPrimitiveIterator it2 = cluster2.iterator();
      while (it2.hasNext()) {
        double theSimilarity = similarity.userSimilarity(userID1, it2.next());
        if (theSimilarity < leastSimilarity) {
          leastSimilarity = theSimilarity;
        }
      }
    }
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      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);
        }
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  private FastByIDMap<List<RecommendedItem>> computeTopRecsPerUserID(List<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;
  }
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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.next()));
    }
   
    TopItems.Estimator<Long> estimator = new Estimator(cluster);
   
    List<RecommendedItem> topItems = TopItems.getTopItems(NUM_CLUSTER_RECS,
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  }
 
  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;
  }
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    @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();
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   *           if an error occurs while listing items
   */
  protected FastIDSet getAllOtherItems(long theUserID) throws TasteException {
    FastIDSet possibleItemsIDs = new FastIDSet();
    FastIDSet itemIDs = dataModel.getItemIDsFromUser(theUserID);
    LongPrimitiveIterator itemIDIterator = itemIDs.iterator();
    while (itemIDIterator.hasNext()) {
      long itemID = itemIDIterator.next();
      PreferenceArray prefs2 = dataModel.getPreferencesForItem(itemID);
      int size2 = prefs2.length();
      for (int j = 0; j < size2; j++) {
        possibleItemsIDs.addAll(dataModel.getItemIDsFromUser(prefs2.getUserID(j)));
      }
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  private void buildAverageDiffs() throws TasteException {
    try {
      buildAveragesLock.writeLock().lock();
      DataModel dataModel = getDataModel();
      LongPrimitiveIterator it = dataModel.getUserIDs();
      while (it.hasNext()) {
        PreferenceArray prefs = dataModel.getPreferencesFromUser(it.nextLong());
        int size = prefs.length();
        for (int i = 0; i < size; i++) {
          long itemID = prefs.getItemID(i);
          RunningAverage average = itemAverages.get(itemID);
          if (average == null) {
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