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 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++;
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      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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    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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    RunningAverage precision = new FullRunningAverage();
    RunningAverage recall = new FullRunningAverage();
    RunningAverage fallOut = new FullRunningAverage();
    LongPrimitiveIterator it = dataModel.getUserIDs();
    while (it.hasNext()) {
      long userID = it.nextLong();
      if (random.nextDouble() < evaluationPercentage) {
        long start = System.currentTimeMillis();
        FastIDSet relevantItemIDs = new FastIDSet(at);
        PreferenceArray prefs = dataModel.getPreferencesFromUser(userID);
        int size = prefs.length();
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   */
  public static FastByIDMap<FastIDSet> toDataMap(DataModel dataModel) throws TasteException {
    FastByIDMap<FastIDSet> data = new FastByIDMap<FastIDSet>(dataModel.getNumUsers());
    LongPrimitiveIterator it = dataModel.getUserIDs();
    while (it.hasNext()) {
      long userID = it.nextLong();
      data.put(userID, dataModel.getItemIDsFromUser(userID));
    }
    return data;
  }

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        if (numRelevantItems > 0) {
          FastByIDMap<PreferenceArray> trainingUsers = new FastByIDMap<PreferenceArray>(dataModel
              .getNumUsers());
          LongPrimitiveIterator it2 = dataModel.getUserIDs();
          while (it2.hasNext()) {
            processOtherUser(userID, relevantItemIDs, trainingUsers, it2
                .nextLong(), dataModel);
          }

          DataModel trainingModel = dataModelBuilder == null ? new GenericDataModel(trainingUsers)
              : dataModelBuilder.buildDataModel(trainingUsers);
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    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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      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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    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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  @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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