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

Examples of org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator.nextLong()

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   /* a hold out test would be better, but this is just a toy example so we only check that the
    * factorization is close to the original matrix */
    RunningAverage avg = new FullRunningAverage();
    LongPrimitiveIterator userIDs = dataModel.getUserIDs();
    while (userIDs.hasNext()) {
      long userID = userIDs.nextLong();
      for (Preference pref : dataModel.getPreferencesFromUser(userID)) {
        double rating = pref.getValue();
        double estimate = svdRecommender.estimatePreference(userID, pref.getItemID());
        double err = rating - estimate;
        avg.addDatum(err * err);
 
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      samplingRate);
    while (someUsers.hasNext()) {
      long userID1 = someUsers.next();
      LongPrimitiveIterator it2 = cluster2.iterator();
      while (it2.hasNext()) {
        double theSimilarity = similarity.userSimilarity(userID1, it2.nextLong());
        if (theSimilarity < leastSimilarity) {
          leastSimilarity = theSimilarity;
        }
      }
    }
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  @Override
  public long[] allSimilarItemIDs(long itemID) throws TasteException {
    FastIDSet allSimilarItemIDs = new FastIDSet();
    LongPrimitiveIterator allItemIDs = dataModel.getItemIDs();
    while (allItemIDs.hasNext()) {
      long possiblySimilarItemID = allItemIDs.nextLong();
      if (!Double.isNaN(itemSimilarity(itemID, possiblySimilarItemID))) {
        allSimilarItemIDs.add(possiblySimilarItemID);
      }
    }
    return allSimilarItemIDs.toArray();
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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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    }

    LongPrimitiveIterator userIDs = dataModel.getUserIDs();
    int index=0;
    while (userIDs.hasNext()) {
      long userID = userIDs.nextLong();
      PreferenceArray preferencesFromUser = dataModel.getPreferencesFromUser(userID);
      for (Preference preference : preferencesFromUser) {
        assertTrue(checked.get(preference.getUserID()).get(preference.getItemID()));
        index++;
      }
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      sum += regularization;
    }

    itemIDs = dataModel.getItemIDs();
    while (itemIDs.hasNext()) {
      long itemID = itemIDs.nextLong();
      Vector itemVector = new DenseVector(factorization.getUserFeatures(itemID));
      double regularization = itemVector.dot(itemVector);
      sum += regularization;
    }
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    /* a hold out test would be better, but this is just a toy example so we only check that the
     * factorization is close to the original matrix */
    RunningAverage avg = new FullRunningAverage();
    LongPrimitiveIterator userIDs = dataModel.getUserIDs();
    while (userIDs.hasNext()) {
      long userID = userIDs.nextLong();
      for (Preference pref : dataModel.getPreferencesFromUser(userID)) {
        double rating = pref.getValue();
        double estimate = svdRecommender.estimatePreference(userID, pref.getItemID());
        double err = rating - estimate;
        avg.addDatum(err * err);
 
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      sum += regularization;
    }

    itemIDs = dataModel.getItemIDs();
    while (itemIDs.hasNext()) {
      long itemID = itemIDs.nextLong();
      Vector itemVector = new DenseVector(factorization.getUserFeatures(itemID));
      double regularization = itemVector.dot(itemVector);
      sum += regularization;
    }
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    /* a hold out test would be better, but this is just a toy example so we only check that the
     * factorization is close to the original matrix */
    RunningAverage avg = new FullRunningAverage();
    LongPrimitiveIterator userIDs = dataModel.getUserIDs();
    while (userIDs.hasNext()) {
      long userID = userIDs.nextLong();
      for (Preference pref : dataModel.getPreferencesFromUser(userID)) {
        double rating = pref.getValue();
        double estimate = svdRecommender.estimatePreference(userID, pref.getItemID());
        double err = rating - estimate;
        avg.addDatum(err * err);
 
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      preferredItemIDsIterator =
          new SamplingLongPrimitiveIterator(preferredItemIDsIterator, samplingRate);
    }
    FastIDSet possibleItemsIDs = new FastIDSet();
    while (preferredItemIDsIterator.hasNext()) {
      long itemID = preferredItemIDsIterator.nextLong();
      PreferenceArray prefs = dataModel.getPreferencesForItem(itemID);
      int prefsLength = prefs.length();
      if (prefsLength > maxUsersPerItem) {
        Iterator<Preference> sampledPrefs =
            new FixedSizeSamplingIterator<Preference>(maxUsersPerItem, prefs.iterator());
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