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

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

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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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          }
          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));
                }
              }
              ((FastByIDMap<PreferenceArray>) data).put(userID, newPrefs);
            }
          }
        }

        removeTimestamp(userID, itemID, timestamps);

      } 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);
          } 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 (relevantItemIDs.contains(pref.getItemID())) {
          iterator.remove();
        }
      }
      if (!prefs2.isEmpty()) {
        trainingUsers.put(otherUserID, new GenericUserPreferenceArray(prefs2));
      }
    } else {
      // otherwise just add all those other user's prefs
      trainingUsers.put(otherUserID, prefs2Array);
    }
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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))
        .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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  @Before
  public void setUp() throws Exception {
    super.setUp();
    FastByIDMap<PreferenceArray> userData = new FastByIDMap<PreferenceArray>();

    userData.put(1L, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(1L, 1L, 5.0f),
                                                                  new GenericPreference(1L, 2L, 5.0f),
                                                                  new GenericPreference(1L, 3L, 2.0f))));

    userData.put(2L, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(2L, 1L, 2.0f),
                                                                  new GenericPreference(2L, 3L, 3.0f),
                                                                  new GenericPreference(2L, 4L, 5.0f))));

    userData.put(3L, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(3L, 2L, 5.0f),
                                                                  new GenericPreference(3L, 4L, 3.0f))));

    userData.put(4L, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(4L, 1L, 3.0f),
                                                                  new GenericPreference(4L, 4L, 5.0f))));

    dataModel = new GenericDataModel(userData);
    factorizer = new ALSWRFactorizer(dataModel, 3, 0.065, 10);
  }
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        if (random.nextDouble() <= sparsity) {
          row.add(new GenericPreference(userIndex, itemIndex, (float) ratings.get(userIndex, itemIndex)));
        }
      }

      userData.put(userIndex, new GenericUserPreferenceArray(row));
    }

    dataModel = new GenericDataModel(userData);
  }
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    this.lambda = 0.01;
    this.numIterations = 1000;

    FastByIDMap<PreferenceArray> userData = new FastByIDMap<PreferenceArray>();

    userData.put(1L, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(1L, 1L, 5.0f),
        new GenericPreference(1L, 2L, 5.0f),
        new GenericPreference(1L, 3L, 2.0f))));

    userData.put(2L, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(2L, 1L, 2.0f),
        new GenericPreference(2L, 3L, 3.0f),
        new GenericPreference(2L, 4L, 5.0f))));

    userData.put(3L, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(3L, 2L, 5.0f),
        new GenericPreference(3L, 4L, 3.0f))));

    userData.put(4L, new GenericUserPreferenceArray(Arrays.asList(new GenericPreference(4L, 1L, 3.0f),
        new GenericPreference(4L, 4L, 5.0f))));
    dataModel = new GenericDataModel(userData);
  }
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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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    DataModel dataModel = EasyMock.createMock(DataModel.class);
    EasyMock.expect(dataModel.getNumItems()).andReturn(3);
    EasyMock.expect(dataModel.getItemIDs()).andReturn(allItemIDs.iterator());

    PreferenceArray prefArrayOfUser123 = new GenericUserPreferenceArray(Collections.singletonList(
        new GenericPreference(123L, 2L, 1.0f)));

    CandidateItemsStrategy strategy = new AllUnknownItemsCandidateItemsStrategy();

    EasyMock.replay(dataModel);
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        }
        oneUserTestPrefs.add(newPref);
      }
    }
    if (oneUserTrainingPrefs != null) {
      trainingPrefs.put(userID, new GenericUserPreferenceArray(oneUserTrainingPrefs));
      if (oneUserTestPrefs != null) {
        testPrefs.put(userID, new GenericUserPreferenceArray(oneUserTestPrefs));
      }
    }
  }
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