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

Examples of org.apache.mahout.cf.taste.impl.common.FastIDSet.addAll()


    FastIDSet userIDs = preferenceForItems.get(itemIDs[0]);
    if (userIDs == null) {
      throw new NoSuchItemException();
    }
    FastIDSet intersection = new FastIDSet(userIDs.size());
    intersection.addAll(userIDs);
    int i = 1;
    while (!intersection.isEmpty() && (i < itemIDs.length)) {
      userIDs = preferenceForItems.get(itemIDs[i]);
      if (userIDs == null) {
        throw new NoSuchItemException();
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        }
      }
     
      // Make new merged cluster
      FastIDSet merged = new FastIDSet(cluster1.size() + cluster2.size());
      merged.addAll(cluster1);
      merged.addAll(cluster2);
     
      // Compare against other clusters; update queue if needed
      // That new pair we're just adding might be pretty close to something else, so
      // catch that case here and put it back into our queue
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      }
     
      // Make new merged cluster
      FastIDSet merged = new FastIDSet(cluster1.size() + cluster2.size());
      merged.addAll(cluster1);
      merged.addAll(cluster2);
     
      // Compare against other clusters; update queue if needed
      // That new pair we're just adding might be pretty close to something else, so
      // catch that case here and put it back into our queue
      for (FastIDSet cluster : clusters) {
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    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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  protected FastIDSet getAllOtherItems(long[] theNeighborhood, long theUserID) throws TasteException {
    DataModel dataModel = getDataModel();
    FastIDSet possibleItemIDs = new FastIDSet();
    for (long userID : theNeighborhood) {
      possibleItemIDs.addAll(dataModel.getItemIDsFromUser(userID));
    }
    possibleItemIDs.removeAll(dataModel.getItemIDsFromUser(theUserID));
    return possibleItemIDs;
  }
 
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        FastIDSet cluster2 = nearestPair.getSecond();
        while (clusterSimilarity.getSimilarity(cluster1, cluster2) >= clusteringThreshold) {
          newClusters.remove(cluster1);
          newClusters.remove(cluster2);
          FastIDSet merged = new FastIDSet(cluster1.size() + cluster2.size());
          merged.addAll(cluster1);
          merged.addAll(cluster2);
          newClusters.add(merged);
          nearestPair = findNearestClusters(newClusters);
          if (nearestPair == null) {
            break;
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        while (clusterSimilarity.getSimilarity(cluster1, cluster2) >= clusteringThreshold) {
          newClusters.remove(cluster1);
          newClusters.remove(cluster2);
          FastIDSet merged = new FastIDSet(cluster1.size() + cluster2.size());
          merged.addAll(cluster1);
          merged.addAll(cluster2);
          newClusters.add(merged);
          nearestPair = findNearestClusters(newClusters);
          if (nearestPair == null) {
            break;
          }
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        FastIDSet cluster1 = nearestPair.getFirst();
        FastIDSet cluster2 = nearestPair.getSecond();
        newClusters.remove(cluster1);
        newClusters.remove(cluster2);
        FastIDSet merged = new FastIDSet(cluster1.size() + cluster2.size());
        merged.addAll(cluster1);
        merged.addAll(cluster2);
        newClusters.add(merged);
      }
    }
  }
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        FastIDSet cluster2 = nearestPair.getSecond();
        newClusters.remove(cluster1);
        newClusters.remove(cluster2);
        FastIDSet merged = new FastIDSet(cluster1.size() + cluster2.size());
        merged.addAll(cluster1);
        merged.addAll(cluster2);
        newClusters.add(merged);
      }
    }
  }
 
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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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