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 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);
      }
    }
  }
 
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  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();
  }
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      List<FastIDSet> clusters = Lists.newArrayList();
      // Begin with a cluster for each user:
      LongPrimitiveIterator it = model.getUserIDs();
      while (it.hasNext()) {
        FastIDSet newCluster = new FastIDSet();
        newCluster.add(it.nextLong());
        clusters.add(newCluster);
      }

      boolean done = false;
      while (!done) {
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    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.nextLong(), recs);
      }
    }
    return recsPerUser;
  }
 
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    DataModel dataModel = getDataModel();
    FastIDSet possibleItemIDs = new FastIDSet();
    LongPrimitiveIterator it = cluster.iterator();
    while (it.hasNext()) {
      possibleItemIDs.addAll(dataModel.getItemIDsFromUser(it.nextLong()));
    }
   
    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.nextLong(), cluster);
      }
    }
    return clustersPerUser;
  }
 
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    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.nextLong(), itemID);
        if (pref != null) {
          average.addDatum(pref);
        }
      }
      return average.getAverage();
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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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        /* start with calculating X^TX or Y^TX */
        log.info("Calculating Y^TY");
        reCalculateTrans(recomputeUserFeatures);
        log.info("Building callables for users.");
        while (userIds.hasNext()) {
          long userId = userIds.nextLong();
          int useridx = userIndex(userId);
          buildCallables(buildConfidenceMatrixForUser(userId), buildPreferenceVectorForUser(userId), useridx);
        }
        finishProcessing();
      } else {
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        /* start with calculating X^TX or Y^TX */
        log.info("Calculating X^TX");
        reCalculateTrans(recomputeUserFeatures);
        log.info("Building callables for items.");
        while (itemIds.hasNext()) {
          long itemId = itemIds.nextLong();
          int itemidx = itemIndex(itemId);
          buildCallables(buildConfidenceMatrixForItem(itemId), buildPreferenceVectorForItem(itemId), itemidx);
        }
        finishProcessing();
      }
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