Package org.grouplens.lenskit.data.dao

Examples of org.grouplens.lenskit.data.dao.EventCollectionDAO

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        rs.add(Ratings.make(42, 6, 2));
        rs.add(Ratings.make(42, 7, 2));
        rs.add(Ratings.make(42, 8, 3));
        rs.add(Ratings.make(42, 9, 1));

        dao = new EventCollectionDAO(rs);
        userDAO = new PrefetchingUserEventDAO(dao);
        qtz = new ValueArrayQuantizer(new double[]{1.0, 2.0, 3.0});

    }
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        rs.add(Ratings.make(1, 5, 2));
        rs.add(Ratings.make(1, 7, 4));
        rs.add(Ratings.make(8, 4, 5));
        rs.add(Ratings.make(8, 5, 4));

        EventDAO dao = new EventCollectionDAO(rs);

        LenskitConfiguration config = new LenskitConfiguration();
        config.bind(EventDAO.class).to(dao);
        config.bind(ItemScorer.class).to(SlopeOneItemScorer.class);
        config.bind(PreferenceDomain.class).to(new PreferenceDomain(1, 5));
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        rs.add(Ratings.make(4, 8, 2));
        rs.add(Ratings.make(5, 8, 3));
        rs.add(Ratings.make(6, 8, 2));
        rs.add(Ratings.make(1, 9, 3));
        rs.add(Ratings.make(3, 9, 4));
        EventCollectionDAO dao = new EventCollectionDAO(rs);
        LenskitConfiguration config = new LenskitConfiguration();
        config.bind(EventDAO.class).to(dao);
        config.bind(ItemScorer.class).to(ItemItemScorer.class);
        // this is the default
        config.bind(UserVectorNormalizer.class)
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        List<Rating> rs = new ArrayList<Rating>();
        rs.add(Ratings.make(1, 5, 2));
        rs.add(Ratings.make(1, 7, 4));
        rs.add(Ratings.make(8, 4, 5));
        rs.add(Ratings.make(8, 5, 4));
        EventDAO dao = new EventCollectionDAO(rs);

        LenskitConfiguration config = new LenskitConfiguration();
        config.bind(EventDAO.class).to(dao);
        config.bind(ItemItemModel.class).toProvider(NormalizingItemItemModelBuilder.class);
        config.bind(ItemScorer.class).to(ItemItemScorer.class);
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        rs.add(Ratings.make(1, 5, 2));
        rs.add(Ratings.make(1, 7, 4));
        rs.add(Ratings.make(8, 4, 5));
        rs.add(Ratings.make(8, 5, 4));

        EventDAO dao = new EventCollectionDAO(rs);

        LenskitConfiguration config = new LenskitConfiguration();
        config.bind(EventDAO.class).to(dao);
        config.bind(ItemScorer.class).to(UserUserItemScorer.class);
        config.bind(NeighborFinder.class).to(LiveNeighborFinder.class);
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        rs.add(Ratings.make(5, 8, 3));
        rs.add(Ratings.make(6, 8, 2));
        rs.add(Ratings.make(1, 9, 3));
        rs.add(Ratings.make(3, 9, 4));

        dao = new EventCollectionDAO(rs);
        LenskitConfiguration config = new LenskitConfiguration();
        config.bind(PreferenceSnapshot.class).to(PackedPreferenceSnapshot.class);
        config.bind(ItemScorer.class).to(FunkSVDItemScorer.class);
        config.bind(BaselineScorer.class, ItemScorer.class)
              .to(UserMeanItemScorer.class);
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        rs.add(Ratings.make(6, 8, 2));
        rs.add(Ratings.make(1, 9, 3));
        rs.add(Ratings.make(3, 9, 4));
        rs.add(Ratings.make(6, 9, 4));
        rs.add(Ratings.make(5, 9, 4));
        EventDAO dao = new EventCollectionDAO(rs);
        LenskitConfiguration config = new LenskitConfiguration();
        config.bind(EventDAO.class).to(dao);
        config.bind(ItemScorer.class).to(UserUserItemScorer.class);
        config.bind(NeighborFinder.class).to(LiveNeighborFinder.class);
        config.within(UserSimilarity.class)
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        List<Rating> rs = new ArrayList<Rating>();
        rs.add(Ratings.make(1, 5, 2));
        rs.add(Ratings.make(1, 7, 4));
        rs.add(Ratings.make(8, 4, 5));
        rs.add(Ratings.make(8, 5, 4));
        EventDAO dao = new EventCollectionDAO(rs);

        LenskitConfiguration config = new LenskitConfiguration();
        config.bind(EventDAO.class).to(dao);
        config.bind(ItemScorer.class).to(ItemItemScorer.class);
        config.bind(GlobalItemScorer.class).to(ItemItemGlobalScorer.class);
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        List<Rating> rs = new ArrayList<Rating>();
        rs.add(Ratings.make(1, 5, 2));
        rs.add(Ratings.make(1, 7, 4));
        rs.add(Ratings.make(8, 4, 5));
        rs.add(Ratings.make(8, 5, 4));
        EventDAO dao = new EventCollectionDAO(rs);

        LenskitConfiguration config = new LenskitConfiguration();
        config.bind(EventDAO.class).to(dao);
        config.bind(ItemScorer.class).to(ItemItemScorer.class);
        config.bind(GlobalItemScorer.class).to(ItemItemGlobalScorer.class);
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        rs.add(Ratings.make(3, 7, 1));
        rs.add(Ratings.make(4, 1, 1));
        rs.add(Ratings.make(4, 5, 1));
        rs.add(Ratings.make(4, 7, 1));
        rs.add(Ratings.make(4, 10, 1));
        EventCollectionDAO dao = new EventCollectionDAO(rs);
        LenskitConfiguration config = new LenskitConfiguration();
        config.bind(EventDAO.class).to(dao);
        config.bind(GlobalItemScorer.class).to(ItemItemGlobalScorer.class);
        // this is the default
        config.bind(UserVectorNormalizer.class)
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Related Classes of org.grouplens.lenskit.data.dao.EventCollectionDAO

  • org.grouplens.lenskit.knn.item.ItemItemGlobalRecommenderTest
  • org.grouplens.lenskit.knn.item.ItemItemRecommenderBuildTest
  • org.grouplens.lenskit.knn.item.ItemItemRecommenderTest
  • org.grouplens.lenskit.knn.item.ItemwiseRecommenderBuildTest
  • org.grouplens.lenskit.knn.item.model.NormalizingItemItemRecommenderBuildTest
  • org.grouplens.lenskit.knn.user.UserUserRecommenderBuildTest
  • org.grouplens.lenskit.knn.user.UserUserRecommenderTest
  • org.grouplens.lenskit.mf.funksvd.FunkSVDRecommenderTest
  • org.grouplens.lenskit.predict.ordrec.OrdRecRatingPredictorTest
  • org.grouplens.lenskit.slopeone.SlopeOneItemRecommenderTest
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