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

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

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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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    if (itemIDs == null) {
      throw new NoSuchUserException();
    }
    PreferenceArray prefArray = new BooleanUserPreferenceArray(itemIDs.size());
    int i = 0;
    LongPrimitiveIterator it = itemIDs.iterator();
    while (it.hasNext()) {
      prefArray.setUserID(i, userID);
      prefArray.setItemID(i, it.next());
      i++;
    }
    return prefArray;
  }
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    if (userIDs == null) {
      throw new NoSuchItemException();
    }
    PreferenceArray prefArray = new BooleanItemPreferenceArray(userIDs.size());
    int i = 0;
    LongPrimitiveIterator it = userIDs.iterator();
    while (it.hasNext()) {
      prefArray.setUserID(i, it.next());
      prefArray.setItemID(i, itemID);
      i++;
    }
    return prefArray;
  }
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    log.info("Building average diffs...");
    try {
      buildAverageDiffsLock.writeLock().lock();
      averageDiffs.clear();
      long averageCount = 0L;
      LongPrimitiveIterator it = dataModel.getUserIDs();
      while (it.hasNext()) {
        averageCount = processOneUser(averageCount, it.nextLong());
      }
     
      pruneInconsequentialDiffs();
      updateAllRecommendableItems();
     
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  private void updateAllRecommendableItems() throws TasteException {
    FastIDSet ids = new FastIDSet(dataModel.getNumItems());
    for (Map.Entry<Long,FastByIDMap<RunningAverage>> entry : averageDiffs.entrySet()) {
      ids.add(entry.getKey());
      LongPrimitiveIterator it = entry.getValue().keySetIterator();
      while (it.hasNext()) {
        ids.add(it.next());
      }
    }
    allRecommendableItemIDs.clear();
    allRecommendableItemIDs.addAll(ids);
    allRecommendableItemIDs.rehash();
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    FastByIDMap<PreferenceArray> trainingUsers = new FastByIDMap<PreferenceArray>(
        1 + (int) (evaluationPercentage * numUsers));
    FastByIDMap<PreferenceArray> testUserPrefs = new FastByIDMap<PreferenceArray>(
        1 + (int) (evaluationPercentage * numUsers));
   
    LongPrimitiveIterator it = dataModel.getUserIDs();
    while (it.hasNext()) {
      long userID = it.nextLong();
      if (random.nextDouble() < evaluationPercentage) {
        processOneUser(trainingPercentage, trainingUsers, testUserPrefs, userID, dataModel);
      }
    }
   
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  }
 
  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();
  }
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  public static void runLoad(Recommender recommender) throws TasteException {
    DataModel dataModel = recommender.getDataModel();
    int numUsers = dataModel.getNumUsers();
    double sampleRate = 1000.0 / numUsers;
    LongPrimitiveIterator userSampler = SamplingLongPrimitiveIterator.maybeWrapIterator(dataModel
        .getUserIDs(), sampleRate);
    recommender.recommend(userSampler.next(), 10); // Warm up
    Collection<Callable<Void>> callables = new ArrayList<Callable<Void>>();
    while (userSampler.hasNext()) {
      callables.add(new LoadCallable(recommender, userSampler.next()));
    }
    AbstractDifferenceRecommenderEvaluator.execute(callables, new AtomicInteger());
  }
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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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public final class AllUnknownItemsCandidateItemsStrategy implements CandidateItemsStrategy {

  @Override
  public FastIDSet getCandidateItems(long userID, DataModel dataModel) throws TasteException {
    FastIDSet possibleItemIDs = new FastIDSet(dataModel.getNumItems());
    LongPrimitiveIterator allItemIDs = dataModel.getItemIDs();
    while (allItemIDs.hasNext()) {
      possibleItemIDs.add(allItemIDs.nextLong());
    }
    possibleItemIDs.removeAll(dataModel.getItemIDsFromUser(userID));
    return possibleItemIDs;
  }
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