Package it.unimi.dsi.fastutil.longs

Examples of it.unimi.dsi.fastutil.longs.LongIterator

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     */
    private LongSet findCandidateNeighbors(long user, SparseVector uvec, LongCollection itemSet) {
        LongSet users = new LongOpenHashSet(100);
        LongSet userItems = uvec.keySet();

        LongIterator items;
        if (userItems.size() < itemSet.size()) {
            items = userItems.iterator();
        } else {
            items = itemSet.iterator();
        }
        while (items.hasNext()) {
            LongSet iusers = itemDAO.getUsersForItem(items.nextLong());
            if (iusers != null) {
                users.addAll(iusers);
            }
        }
        users.remove(user);
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            assert matrix.size() == i;
            final long rowItem = itemDomain.getKey(i);
            final SparseVector vec1 = buildContext.itemVector(rowItem);

            // Take advantage of sparsity if we can
            LongIterator neighbors = iterationStrategy.neighborIterator(buildContext, rowItem, false);
            currentRow.fill(0);

            // Compute similarities and populate the vector
            while (neighbors.hasNext()) {
                final long colItem = neighbors.nextLong();
                final SparseVector vec2 = buildContext.itemVector(colItem);
                assert currentRow.containsKey(colItem);
                currentRow.set(colItem, similarity.similarity(rowItem, vec1, colItem, vec2));
            }
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        BinaryIndexTableWriter tableWriter =
                BinaryIndexTableWriter.create(format, channel, keys.size());

        SortComparator indexComparator = new SortComparator();

        LongIterator iter = keys.iterator();
        while (iter.hasNext()) {
            final long key = iter.nextLong();
            int[] indexes = map.get(key).toIntArray();
            if (needsSorting) {
                IntArrays.quickSort(indexes, indexComparator);
            }
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        final double lg2 = log(2);

        double gain = 0;
        int rank = 0;

        LongIterator iit = items.iterator();
        while (iit.hasNext()) {
            final long item = iit.nextLong();
            final double v = values.get(item);
            rank++;
            if (rank < 2) {
                gain += v;
            } else {
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    }

    double computeHLU(LongList items, SparseVector values) {
        double utility = 0;
        int rank = 0;
        LongIterator itemIterator = items.iterator();
        while (itemIterator.hasNext()) {
            final double v = values.get(itemIterator.nextLong());
            rank++;
            utility += v / Math.pow(2, (rank - 1) / (alpha - 1));
        }
        return utility;
    }
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        final double lg2 = log(2);

        double gain = 0;
        int rank = 0;

        LongIterator iit = items.iterator();
        while (iit.hasNext()) {
            final long item = iit.nextLong();
            final double v = values.get(item, 0);
            rank++;
            if (rank < 2) {
                gain += v;
            } else {
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     * Constructs and returns a {@link SlopeOneModel}.
     */
    @Override
    public SlopeOneModel get() {
        LongSet items = buildContext.getItems();
        LongIterator outer = items.iterator();
        while (outer.hasNext()) {
            final long item1 = outer.nextLong();
            final SparseVector vec1 = buildContext.itemVector(item1);
            LongIterator inner = items.iterator();
            while (inner.hasNext()) {
                final long item2 = inner.nextLong();
                if (item1 != item2) {
                    SparseVector vec2 = buildContext.itemVector(item2);
                    accumulator.putItemPair(item1, vec1, item2, vec2);
                }
            }
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        for (VectorEntry e : scores.view(VectorEntry.State.EITHER)) {
            final long predicteeItem = e.getKey();
            if (!user.containsKey(predicteeItem)) {
                double total = 0;
                int nitems = 0;
                LongIterator ratingIter = user.keySet().iterator();
                while (ratingIter.hasNext()) {
                    long currentItem = ratingIter.nextLong();
                    int nusers = model.getCoratings(predicteeItem, currentItem);
                    if (nusers != 0) {
                        double currentDev = model.getDeviation(predicteeItem, currentItem);
                        total += currentDev + user.get(currentItem);
                        nitems++;
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        for (VectorEntry e : scores.view(VectorEntry.State.EITHER)) {
            final long predicteeItem = e.getKey();
            if (!ratings.containsKey(predicteeItem)) {
                double total = 0;
                int nusers = 0;
                LongIterator ratingIter = ratings.keySet().iterator();
                while (ratingIter.hasNext()) {
                    long currentItem = ratingIter.nextLong();
                    double currentDev = model.getDeviation(predicteeItem, currentItem);
                    if (!Double.isNaN(currentDev)) {
                        int weight = model.getCoratings(predicteeItem, currentItem);
                        total += (currentDev + ratings.get(currentItem)) * weight;
                        nusers += weight;
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        ratings = snap.getRatings();
        domain = dom;
        estimates = new double[ratings.size()];

        final LongCollection userIds = snap.getUserIds();
        LongIterator userIter = userIds.iterator();
        while (userIter.hasNext()) {
            long uid = userIter.nextLong();
            SparseVector rvector = snap.userRatingVector(uid);
            MutableSparseVector blpreds = MutableSparseVector.create(rvector.keySet());
            baseline.score(uid, blpreds);

            for (IndexedPreference r : snap.getUserRatings(uid)) {
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