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

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

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

org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator
Adds notion of iterating over long primitives in the style of an {@link Iterator} -- asopposed to iterating over {@link Long}. Implementations of this interface however also implement {@link Iterator} and {@link Iterable} over {@link Long} for convenience.

      entry.getValue().sortByUser();
    }


    this.userIDs = new long[userData.size()];
    int i = 0;
    LongPrimitiveIterator it = userData.keySetIterator();
    while (it.hasNext()) {
      userIDs[i++] = it.next();
    }
    Arrays.sort(userIDs);


    this.timestamps = timestamps;
  }

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   * @return a {@link FastByIDMap} mapping user IDs to {@link PreferenceArray}s representing
   *  that user's preferences
   */
  public static FastByIDMap<PreferenceArray> toDataMap(DataModel dataModel) throws TasteException {
    FastByIDMap<PreferenceArray> data = new FastByIDMap<PreferenceArray>(dataModel.getNumUsers());
    LongPrimitiveIterator it = dataModel.getUserIDs();
    while (it.hasNext()) {
      long userID = it.nextLong();
      data.put(userID, dataModel.getPreferencesFromUser(userID));
    }
    return data;
  }

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    int numUsers = dataModel.getNumUsers();
    userMap = new FastByIDMap<Integer>(numUsers);
    
    int idx = 0;
    LongPrimitiveIterator userIterator = dataModel.getUserIDs();
    while (userIterator.hasNext()) {
      userMap.put(userIterator.nextLong(), idx++);
    }
    
    int numItems = dataModel.getNumItems();
    itemMap = new FastByIDMap<Integer>(numItems);
    
    idx = 0;
    LongPrimitiveIterator itemIterator = dataModel.getItemIDs();
    while (itemIterator.hasNext()) {
      itemMap.put(itemIterator.nextLong(), idx++);
    }
    
    double average = getAveragePreference();
    double defaultValue = Math.sqrt((average - 1.0) / numFeatures);

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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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    } else {


      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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  private FastByIDMap<List<RecommendedItem>> computeTopRecsPerUserID(Iterable<FastIDSet> clusters)
    throws TasteException {
    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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  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.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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    @Override
    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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