Examples of org.apache.mahout.cf.taste.impl.model.GenericUserPreferenceArray$PreferenceArrayIterator

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

Examples of org.apache.mahout.cf.taste.impl.model.GenericUserPreferenceArray$PreferenceArrayIterator

org.apache.mahout.cf.taste.impl.model.GenericUserPreferenceArray

This implementation maintains two parallel arrays, of user IDs and values. The idea is to save allocating {@link Preference} objects themselves. This saves the overhead of {@link Preference} objects but alsoduplicating the user ID value.
@see BooleanUserPreferenceArray @see GenericItemPreferenceArray @see GenericPreference

    ItemSimilarity itemSimilarity = EasyMock.createMock(ItemSimilarity.class);
    CandidateItemsStrategy candidateItemsStrategy = EasyMock.createMock(CandidateItemsStrategy.class);
    MostSimilarItemsCandidateItemsStrategy mostSimilarItemsCandidateItemsStrategy =
        EasyMock.createMock(MostSimilarItemsCandidateItemsStrategy.class);


    PreferenceArray preferencesFromUser = new GenericUserPreferenceArray(
        Arrays.asList(new GenericPreference(1L, 1L, 5.0f), new GenericPreference(1L, 2L, 4.0f)));


    EasyMock.expect(dataModel.getMinPreference()).andReturn(Float.NaN);
    EasyMock.expect(dataModel.getMaxPreference()).andReturn(Float.NaN);


    EasyMock.expect(dataModel.getPreferencesFromUser(1L)).andReturn(preferencesFromUser);
    EasyMock.expect(candidateItemsStrategy.getCandidateItems(1L, preferencesFromUser, dataModel))
        .andReturn(new FastIDSet(new long[] { 3L, 4L }));


    EasyMock.expect(itemSimilarity.itemSimilarities(3L, preferencesFromUser.getIDs()))
        .andReturn(new double[] { 0.5, 0.3 });
    EasyMock.expect(itemSimilarity.itemSimilarities(4L, preferencesFromUser.getIDs()))
        .andReturn(new double[] { 0.4, 0.1 });


    EasyMock.replay(dataModel, itemSimilarity, candidateItemsStrategy, mostSimilarItemsCandidateItemsStrategy);


    Recommender recommender = new GenericItemBasedRecommender(dataModel, itemSimilarity,

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          }
        }
        if (exists) {
          rawData.remove(userID);
          if (length > 1) {
            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));
              }
            }
            rawData.put(userID, newPrefs);
          }
          log.info("Removing userID: {} itemID: {}", userID, itemID);

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          }
        }
      }
      if (!exists) {
        if (prefs == null) {
          prefs = new GenericUserPreferenceArray(1);
        } 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 = newPrefs;
        }
        prefs.setUserID(0, userID);
        prefs.setItemID(0, itemID);

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      if (prefs.isEmpty()) {
        throw new NoSuchUserException(userID);
      }


      return new GenericUserPreferenceArray(prefs);


    } catch (SQLException sqle) {
      log.warn("Exception while retrieving user", sqle);
      throw new TasteException(sqle);
    } finally {

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      Long currentUserID = null;
      List<Preference> currentPrefs = Lists.newArrayList();
      while (rs.next()) {
        long nextUserID = getLongColumn(rs, 1);
        if (currentUserID != null && !currentUserID.equals(nextUserID) && !currentPrefs.isEmpty()) {
          result.put(currentUserID, new GenericUserPreferenceArray(currentPrefs));
          currentPrefs.clear();
        }
        currentPrefs.add(buildPreference(rs));
        currentUserID = nextUserID;
      }
      if (!currentPrefs.isEmpty()) {
        result.put(currentUserID, new GenericUserPreferenceArray(currentPrefs));
      }


      return result;


    } catch (SQLException sqle) {

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      List<HColumn<Long,Float>> itemIDColumns = result.getColumns();
      if (itemIDColumns.isEmpty()) {
        throw new NoSuchUserException(userID);
      }
      int size = itemIDColumns.size();
      PreferenceArray prefs = new GenericUserPreferenceArray(size);
      prefs.setUserID(0, userID);
      for (int i = 0; i < size; i++) {
        HColumn<Long,Float> itemIDColumn = itemIDColumns.get(i);
        prefs.setItemID(i, itemIDColumn.getName());
        prefs.setValue(i, itemIDColumn.getValue());
      }
      return prefs;
    }

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      for(int j=0;j<foods.size();j++) {
        if(preferences[i][j] != null) {
          userPreferences.add(new GenericPreference(userId, id2thing.toLongID(foods.get(j)), preferences[i][j]));
        }
      }
      GenericUserPreferenceArray userArray = new GenericUserPreferenceArray(userPreferences);
      preferenceMap.put(userId, userArray);
    }
    model = new GenericDataModel(preferenceMap);
  }

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      }
      
      // create the corresponding mahout data structure from the map
      FastByIDMap<PreferenceArray> preferecesOfUsersFastMap = new FastByIDMap<PreferenceArray>();
      for(Entry<Long, List<Preference>> entry : preferecesOfUsers.entrySet()) {
        preferecesOfUsersFastMap.put(entry.getKey(), new GenericUserPreferenceArray(entry.getValue()));
      }


      // create a data model 
      dataModel = new GenericDataModel(preferecesOfUsersFastMap);

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    EasyMock.expect(dataModel.getPreferencesForItem(1L)).andReturn(preferencesForItem1);
    EasyMock.expect(dataModel.getItemIDsFromUser(123L)).andReturn(itemIDsFromUser123);
    EasyMock.expect(dataModel.getItemIDsFromUser(456L)).andReturn(itemIDsFromUser456);


    PreferenceArray prefArrayOfUser123 =
        new GenericUserPreferenceArray(Collections.singletonList(new GenericPreference(123L, 1L, 1.0f)));


    CandidateItemsStrategy strategy = new PreferredItemsNeighborhoodCandidateItemsStrategy();


    EasyMock.replay(dataModel);

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    ItemSimilarity itemSimilarity = EasyMock.createMock(ItemSimilarity.class);
    CandidateItemsStrategy candidateItemsStrategy = EasyMock.createMock(CandidateItemsStrategy.class);
    MostSimilarItemsCandidateItemsStrategy mostSimilarItemsCandidateItemsStrategy =
        EasyMock.createMock(MostSimilarItemsCandidateItemsStrategy.class);


    PreferenceArray preferencesFromUser = new GenericUserPreferenceArray(
        Arrays.asList(new GenericPreference(1L, 1L, 5.0f), new GenericPreference(1L, 2L, 4.0f)));


    EasyMock.expect(dataModel.getMinPreference()).andReturn(Float.NaN);
    EasyMock.expect(dataModel.getMaxPreference()).andReturn(Float.NaN);


    EasyMock.expect(dataModel.getPreferencesFromUser(1L)).andReturn(preferencesFromUser);
    EasyMock.expect(candidateItemsStrategy.getCandidateItems(1L, preferencesFromUser, dataModel, false))
        .andReturn(new FastIDSet(new long[] { 3L, 4L }));


    EasyMock.expect(itemSimilarity.itemSimilarities(3L, preferencesFromUser.getIDs()))
        .andReturn(new double[] { 0.5, 0.3 });
    EasyMock.expect(itemSimilarity.itemSimilarities(4L, preferencesFromUser.getIDs()))
        .andReturn(new double[] { 0.4, 0.1 });


    EasyMock.replay(dataModel, itemSimilarity, candidateItemsStrategy, mostSimilarItemsCandidateItemsStrategy);


    Recommender recommender = new GenericItemBasedRecommender(dataModel, itemSimilarity,

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