Package org.grouplens.lenskit.vectors

Examples of org.grouplens.lenskit.vectors.SparseVector

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        List<Preference> prefs = new ArrayList<Preference>();
        prefs.add(new SimplePreference(1, 3, 2.5));
        prefs.add(new SimplePreference(1, 9, 4.0));
        prefs.add(new SimplePreference(1, 5, 2.8));
        prefs.add(new SimplePreference(1, 9, Math.PI));
        @SuppressWarnings("unused")
    SparseVector v = Preferences.userPreferenceVector(prefs);
    }
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    public void testUPVBadUser() {
        List<Preference> prefs = new ArrayList<Preference>();
        prefs.add(new SimplePreference(1, 3, 2.5));
        prefs.add(new SimplePreference(1, 9, 4.0));
        prefs.add(new SimplePreference(2, 5, 2.8));
        @SuppressWarnings("unused")
    SparseVector v = Preferences.userPreferenceVector(prefs);
    }
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                (Event) Ratings.make(42, 39, 2.5),
                Ratings.make(42, 62, 3.5),
                Ratings.make(42, 22, 3));
        UserHistory<Event> history = History.forUser(42, events);
        assertThat(history, hasSize(3));
        SparseVector v = history.memoize(RatingVectorUserHistorySummarizer.SummaryFunction.INSTANCE);
        assertThat(v.size(), equalTo(3));
        assertThat(v.mean(), equalTo(3.0));
        assertThat(history.memoize(RatingVectorUserHistorySummarizer.SummaryFunction.INSTANCE),
                   sameInstance(v));
    }
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        Collection<Rating> ratings = new ArrayList<Rating>();
        RatingBuilder rb = new RatingBuilder();
        ratings.add(Ratings.make(7, 5, 3.5));
        ratings.add(Ratings.make(3, 5, 1.5));
        ratings.add(Ratings.make(8, 5, 2));
        SparseVector v = Ratings.itemRatingVector(ratings);
        assertEquals(3, v.size());
        assertEquals(7, v.sum(), EPSILON);

        long[] keys = {3, 7, 8};
        double[] values = {1.5, 3.5, 2};
        SparseVector sv = MutableSparseVector.wrap(keys, values);
        assertEquals(sv, v);
    }
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        Collection<Rating> ratings = Lists.newArrayList(
                Ratings.make(5, 7, 3.5),
                Ratings.make(5, 3, 1.5),
                Ratings.make(5, 8, 2)
        );
        SparseVector v = Ratings.userRatingVector(ratings);
        assertEquals(3, v.size());
        assertEquals(7, v.sum(), EPSILON);

        long[] keys = {3, 7, 8};
        double[] values = {1.5, 3.5, 2};
        SparseVector sv = MutableSparseVector.wrap(keys, values);
        assertEquals(sv, v);
    }
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    public void score(long user, @Nonnull MutableSparseVector scores) {
        UserHistory<? extends Event> history = dao.getEventsForUser(user, summarizer.eventTypeWanted());
        if (history == null) {
            history = History.forUser(user);
        }
        SparseVector summary = summarizer.summarize(history);
        VectorTransformation transform = normalizer.makeTransformation(user, summary);
        MutableSparseVector normed = summary.mutableCopy();
        transform.apply(normed);

        scores.clear();
        algorithm.scoreItems(model, normed, scores, scorer);
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            final long itemId1 = outer.nextLong();
            if (logger.isTraceEnabled()) {
                logger.trace("computing similarities for item {} ({} of {})",
                             itemId1, ndone, nitems);
            }
            SparseVector vec1 = buildContext.itemVector(itemId1);

            LongIterator itemIter = neighborStrategy.neighborIterator(buildContext, itemId1,
                                                                      itemSimilarity.isSymmetric());

            ScoredItemAccumulator row = rows.get(itemId1);
            while (itemIter.hasNext()) {
                long itemId2 = itemIter.nextLong();
                if (itemId1 != itemId2) {
                    SparseVector vec2 = buildContext.itemVector(itemId2);
                    double sim = itemSimilarity.similarity(itemId1, vec1, itemId2, vec2);
                    if (threshold.retain(sim)) {
                        row.put(itemId2, sim);
                        if (itemSimilarity.isSymmetric()) {
                            rows.get(itemId2).put(itemId1, sim);
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    }

    @Override
    public Iterable<Neighbor> getCandidateNeighbors(UserHistory<? extends Event> user, LongSet items) {
        final long uid = user.getUserId();
        SparseVector urs = summarizer.summarize(user);
        final ImmutableSparseVector vector = normalizer.normalize(user.getUserId(), urs, null)
                                                 .freeze();

        LongCollection qset = items;
        if (vector.size() < qset.size()) {
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        }
        @Override
        protected Neighbor computeNext() {
            while (neighborIter.hasNext()) {
                final long neighbor = neighborIter.nextLong();
                SparseVector vector = snapshot.getNormalizedUserVector(neighbor);
                double sim = similarity.similarity(user, userVector, neighbor, vector);
                if (acceptSimilarity(sim)) {
                    return new Neighbor(neighbor, snapshot.getUserVector(neighbor), sim);
                }
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            sizeChan.set(e, count);
            weightChan.set(e,weight);
        }

        // Denormalize and return the results
        SparseVector urv = RatingVectorUserHistorySummarizer.makeRatingVector(history);
        VectorTransformation vo = normalizer.makeTransformation(history.getUserId(), urv);
        vo.unapply(scores);
    }
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Related Classes of org.grouplens.lenskit.vectors.SparseVector

  • it.unimi.dsi.fastutil.doubles.DoubleArrayList
  • it.unimi.dsi.fastutil.doubles.DoubleIterator
  • it.unimi.dsi.fastutil.ints.IntIterator
  • org.grouplens.lenskit.baseline.ConstantItemScorerTest
  • org.grouplens.lenskit.baseline.MeanScorersTest
  • org.grouplens.lenskit.basic.AbstractGlobalItemScorer
  • org.grouplens.lenskit.basic.AbstractItemScorer
  • org.grouplens.lenskit.basic.AbstractRatingPredictor
  • org.grouplens.lenskit.basic.PrecomputedItemScorer
  • org.grouplens.lenskit.basic.RescoringItemRecommender
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