Examples of PreferenceArray

org.apache.mahout.cf.taste.model.PreferenceArray
An alternate representation of an array of {@link Preference}. Implementations, in theory, can produce a more memory-efficient representation.

Examples of org.apache.mahout.cf.taste.model.PreferenceArray

      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);
          addDatumAndCreateIfNeeded(itemID, value, itemAverages);
          addDatumAndCreateIfNeeded(userID, value, userAverages);
          overallAveragePrefValue.addDatum(value);
        }
      }

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Examples of org.apache.mahout.cf.taste.model.PreferenceArray

    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)));
      }
    }
    possibleItemsIDs.removeAll(itemIDs);
    return possibleItemsIDs;
  }

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Examples of org.apache.mahout.cf.taste.model.PreferenceArray

  }
  
  private float doEstimatePreference(long userID, long itemID) throws TasteException {
    double count = 0.0;
    double totalPreference = 0.0;
    PreferenceArray prefs = getDataModel().getPreferencesFromUser(userID);
    RunningAverage[] averages = diffStorage.getDiffs(userID, itemID, prefs);
    int size = prefs.length();
    for (int i = 0; i < size; i++) {
      RunningAverage averageDiff = averages[i];
      if (averageDiff != null) {
        double averageDiffValue = averageDiff.getAverage();
        if (weighted) {
          double weight = averageDiff.getCount();
          if (stdDevWeighted) {
            double stdev = ((RunningAverageAndStdDev) averageDiff).getStandardDeviation();
            if (!Double.isNaN(stdev)) {
              weight /= 1.0 + stdev;
            }
            // If stdev is NaN, then it is because count is 1. Because we're weighting by count,
            // the weight is already relatively low. We effectively assume stdev is 0.0 here and
            // that is reasonable enough. Otherwise, dividing by NaN would yield a weight of NaN
            // and disqualify this pref entirely
            // (Thanks Daemmon)
          }
          totalPreference += weight * (prefs.getValue(i) + averageDiffValue);
          count += weight;
        } else {
          totalPreference += prefs.getValue(i) + averageDiffValue;
          count += 1.0;
        }
      }
    }
    if (count <= 0.0) {

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Examples of org.apache.mahout.cf.taste.model.PreferenceArray

    }
    
    DataModel model = getDataModel();
    TopItems.Estimator<Long> estimator = new RecommendedBecauseEstimator(userID, itemID, similarity);
    
    PreferenceArray prefs = model.getPreferencesFromUser(userID);
    int size = prefs.length();
    FastIDSet allUserItems = new FastIDSet(size);
    for (int i = 0; i < size; i++) {
      allUserItems.add(prefs.getItemID(i));
    }
    allUserItems.remove(itemID);
    
    return TopItems.getTopItems(howMany, allUserItems.iterator(), null, estimator);
  }

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Examples of org.apache.mahout.cf.taste.model.PreferenceArray

                                                   int howMany,
                                                   TopItems.Estimator<Long> estimator) throws TasteException {
    DataModel model = getDataModel();
    FastIDSet possibleItemsIDs = new FastIDSet();
    for (long itemID : itemIDs) {
      PreferenceArray prefs = model.getPreferencesForItem(itemID);
      int size = prefs.length();
      for (int i = 0; i < size; i++) {
        long userID = prefs.get(i).getUserID();
        possibleItemsIDs.addAll(model.getItemIDsFromUser(userID));
      }
    }
    possibleItemsIDs.removeAll(itemIDs);
    return TopItems.getTopItems(howMany, possibleItemsIDs.iterator(), null, estimator);

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Examples of org.apache.mahout.cf.taste.model.PreferenceArray

  
  protected float doEstimatePreference(long userID, long itemID) throws TasteException {
    double preference = 0.0;
    double totalSimilarity = 0.0;
    int count = 0;
    PreferenceArray prefs = getDataModel().getPreferencesFromUser(userID);
    int size = prefs.length();
    for (int i = 0; i < size; i++) {
      double theSimilarity = similarity.itemSimilarity(itemID, prefs.getItemID(i));
      if (!Double.isNaN(theSimilarity)) {
        // Why + 1.0? similarity ranges from -1.0 to 1.0, and we want to use it as a simple
        // weight. To avoid negative values, we add 1.0 to put it in
        // the [0.0,2.0] range which is reasonable for weights
        theSimilarity += 1.0;
        preference += theSimilarity * prefs.getValue(i);
        totalSimilarity += theSimilarity;
        count++;
      }
    }
    // Throw out the estimate if it was based on no data points, of course, but also if based on

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Examples of org.apache.mahout.cf.taste.model.PreferenceArray

    
    // This is kind of gross but need to handle two types of storage
    Object maybePrefs = data.get(userID);
    if (fromPriorData) {
      
      PreferenceArray prefs = (PreferenceArray) maybePrefs;
      if (preferenceValueString.length() == 0) {
        if (prefs != null) {
          boolean exists = false;
          int length = prefs.length();
          for (int i = 0; i < length; i++) {
            if (prefs.getItemID(i) == itemID) {
              exists = true;
              break;
            }
          }
          if (exists) {
            if (length == 1) {
              data.remove(userID);
            } else {
              PreferenceArray newPrefs = new GenericUserPreferenceArray(length - 1);
              for (int i = 0, j = 0; i < length; i++, j++) {
                if (prefs.getItemID(i) == itemID) {
                  j--;
                } else {
                  newPrefs.set(j, prefs.get(i));
                }
              }
            }
          }
        }
        
      } else {
        
        float preferenceValue = Float.parseFloat(preferenceValueString);
        
        boolean exists = false;
        if (prefs != null) {
          for (int i = 0; i < prefs.length(); i++) {
            if (prefs.getItemID(i) == itemID) {
              exists = true;
              prefs.setValue(i, preferenceValue);
              break;
            }
          }
        }
        
        if (!exists) {
          if (prefs == null) {
            prefs = new GenericUserPreferenceArray(1);
            ((FastByIDMap<PreferenceArray>) data).put(userID, prefs);
          } else {
            PreferenceArray newPrefs = new GenericUserPreferenceArray(prefs.length() + 1);
            for (int i = 0, j = 1; i < prefs.length(); i++, j++) {
              newPrefs.set(j, prefs.get(i));
            }
          }
          prefs.setUserID(0, userID);
          prefs.setItemID(0, itemID);
          prefs.setValue(0, preferenceValue);

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Examples of org.apache.mahout.cf.taste.model.PreferenceArray

  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++;
    }
    FastByIDMap<Double> newIufFactors = new FastByIDMap<Double>(itemPreferenceCounts.size());
    double logFactor = Math.log(logBase);

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Examples of org.apache.mahout.cf.taste.model.PreferenceArray

  private class MeanStdevRetriever implements Retriever<Long,RunningAverageAndStdDev> {
    
    @Override
    public RunningAverageAndStdDev get(Long userID) throws TasteException {
      RunningAverageAndStdDev running = new FullRunningAverageAndStdDev();
      PreferenceArray prefs = dataModel.getPreferencesFromUser(userID);
      int size = prefs.length();
      for (int i = 0; i < size; i++) {
        running.addDatum(prefs.getValue(i));
      }
      return running;
    }

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Examples of org.apache.mahout.cf.taste.model.PreferenceArray

    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) {
            average = new FullRunningAverage();
            itemAverages.put(itemID, average);
          }
          average.addDatum(prefs.getValue(i));
        }
      }
    } finally {
      buildAveragesLock.writeLock().unlock();
    }

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