Examples with WritableIntegerDataStore de.lmu.ifi.dbs.elki.database.datastore.WritableIntegerDataStore used on opensource projects

Example 16 with WritableIntegerDataStore

use of de.lmu.ifi.dbs.elki.database.datastore.WritableIntegerDataStore in project elki by elki-project.

the class CLARA method run.

@Override
public Clustering<MedoidModel> run(Database database, Relation<V> relation) {
    if (relation.size() <= 0) {
        return new Clustering<>("CLARA Clustering", "clara-clustering");
    }
    DBIDs ids = relation.getDBIDs();
    DistanceQuery<V> distQ = database.getDistanceQuery(relation, getDistanceFunction());
    double best = Double.POSITIVE_INFINITY;
    ArrayModifiableDBIDs bestmedoids = null;
    WritableIntegerDataStore bestclusters = null;
    Random rnd = random.getSingleThreadedRandom();
    FiniteProgress prog = LOG.isVerbose() ? new FiniteProgress("Processing random samples", numsamples, LOG) : null;
    for (int j = 0; j < numsamples; j++) {
        DBIDs rids = DBIDUtil.randomSample(ids, sampling, rnd);
        // FIXME: precompute and use a distance matrix for this sample!
        // Choose initial medoids
        ArrayModifiableDBIDs medoids = DBIDUtil.newArray(initializer.chooseInitialMedoids(k, rids, distQ));
        // Setup cluster assignment store
        WritableIntegerDataStore assignment = DataStoreUtil.makeIntegerStorage(ids, DataStoreFactory.HINT_HOT | DataStoreFactory.HINT_TEMP, -1);
        new /* PAM */
        Instance(distQ, rids, assignment).run(medoids, maxiter);
        double score = assignRemainingToNearestCluster(medoids, ids, rids, assignment, distQ);
        if (score < best) {
            best = score;
            bestmedoids = medoids;
            bestclusters = assignment;
        }
        LOG.incrementProcessed(prog);
    }
    LOG.ensureCompleted(prog);
    ArrayModifiableDBIDs[] clusters = ClusteringAlgorithmUtil.partitionsFromIntegerLabels(ids, bestclusters, k);
    // Wrap result
    Clustering<MedoidModel> result = new Clustering<>("CLARA Clustering", "clara-clustering");
    for (DBIDArrayIter it = bestmedoids.iter(); it.valid(); it.advance()) {
        MedoidModel model = new MedoidModel(DBIDUtil.deref(it));
        result.addToplevelCluster(new Cluster<>(clusters[it.getOffset()], model));
    }
    return result;
}

Example 17 with WritableIntegerDataStore

use of de.lmu.ifi.dbs.elki.database.datastore.WritableIntegerDataStore in project elki by elki-project.

the class KMeansCompare method run.

@Override
public Clustering<KMeansModel> run(Database database, Relation<V> relation) {
    if (relation.size() <= 0) {
        return new Clustering<>("k-Means Clustering", "kmeans-clustering");
    }
    // Choose initial means
    if (LOG.isStatistics()) {
        LOG.statistics(new StringStatistic(KEY + ".initialization", initializer.toString()));
    }
    double[][] means = initializer.chooseInitialMeans(database, relation, k, getDistanceFunction());
    // Setup cluster assignment store
    List<ModifiableDBIDs> clusters = new ArrayList<>();
    for (int i = 0; i < k; i++) {
        clusters.add(DBIDUtil.newHashSet((int) (relation.size() * 2. / k)));
    }
    WritableIntegerDataStore assignment = DataStoreUtil.makeIntegerStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, -1);
    double[] varsum = new double[k];
    // Cluster distances
    double[][] cdist = new double[k][k];
    IndefiniteProgress prog = LOG.isVerbose() ? new IndefiniteProgress("K-Means iteration", LOG) : null;
    DoubleStatistic varstat = LOG.isStatistics() ? new DoubleStatistic(this.getClass().getName() + ".variance-sum") : null;
    LongStatistic diststat = LOG.isStatistics() ? new LongStatistic(KEY + ".distance-computations") : null;
    int iteration = 0;
    for (; maxiter <= 0 || iteration < maxiter; iteration++) {
        LOG.incrementProcessed(prog);
        recomputeSeperation(means, cdist, diststat);
        boolean changed = assignToNearestCluster(relation, means, clusters, assignment, varsum, cdist, diststat);
        logVarstat(varstat, varsum);
        if (LOG.isStatistics()) {
            LOG.statistics(diststat);
        }
        // Stop if no cluster assignment changed.
        if (!changed) {
            break;
        }
        // Recompute means.
        means = means(clusters, means, relation);
    }
    LOG.setCompleted(prog);
    if (LOG.isStatistics()) {
        LOG.statistics(new LongStatistic(KEY + ".iterations", iteration));
    }
    // Wrap result
    Clustering<KMeansModel> result = new Clustering<>("k-Means Clustering", "kmeans-clustering");
    for (int i = 0; i < clusters.size(); i++) {
        DBIDs ids = clusters.get(i);
        if (ids.size() == 0) {
            continue;
        }
        KMeansModel model = new KMeansModel(means[i], varsum[i]);
        result.addToplevelCluster(new Cluster<>(ids, model));
    }
    return result;
}

Example 18 with WritableIntegerDataStore

use of de.lmu.ifi.dbs.elki.database.datastore.WritableIntegerDataStore in project elki by elki-project.

the class KMeansHamerly method run.

@Override
public Clustering<KMeansModel> run(Database database, Relation<V> relation) {
    if (relation.size() <= 0) {
        return new Clustering<>("k-Means Clustering", "kmeans-clustering");
    }
    // Choose initial means
    if (LOG.isStatistics()) {
        LOG.statistics(new StringStatistic(KEY + ".initialization", initializer.toString()));
    }
    double[][] means = initializer.chooseInitialMeans(database, relation, k, getDistanceFunction());
    // Setup cluster assignment store
    List<ModifiableDBIDs> clusters = new ArrayList<>();
    for (int i = 0; i < k; i++) {
        clusters.add(DBIDUtil.newHashSet((int) (relation.size() * 2. / k)));
    }
    WritableIntegerDataStore assignment = DataStoreUtil.makeIntegerStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, -1);
    // Hamerly bounds
    WritableDoubleDataStore upper = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, Double.POSITIVE_INFINITY);
    WritableDoubleDataStore lower = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, 0.);
    // Storage for updated means:
    final int dim = means[0].length;
    double[][] sums = new double[k][dim];
    // Separation of means / distance moved.
    double[] sep = new double[k];
    IndefiniteProgress prog = LOG.isVerbose() ? new IndefiniteProgress("K-Means iteration", LOG) : null;
    LongStatistic rstat = LOG.isStatistics() ? new LongStatistic(KEY + ".reassignments") : null;
    int iteration = 0;
    for (; maxiter <= 0 || iteration < maxiter; iteration++) {
        LOG.incrementProcessed(prog);
        int changed;
        if (iteration == 0) {
            changed = initialAssignToNearestCluster(relation, means, sums, clusters, assignment, upper, lower);
        } else {
            recomputeSeperation(means, sep);
            changed = assignToNearestCluster(relation, means, sums, clusters, assignment, sep, upper, lower);
        }
        if (rstat != null) {
            rstat.setLong(changed);
            LOG.statistics(rstat);
        }
        // Stop if no cluster assignment changed.
        if (changed == 0) {
            break;
        }
        // Recompute means.
        for (int i = 0; i < k; i++) {
            final int s = clusters.get(i).size();
            timesEquals(sums[i], s > 0 ? 1. / s : 1.);
        }
        double delta = maxMoved(means, sums, sep);
        updateBounds(relation, assignment, upper, lower, sep, delta);
        for (int i = 0; i < k; i++) {
            final int s = clusters.get(i).size();
            System.arraycopy(sums[i], 0, means[i], 0, dim);
            // Restore to sum for next iteration
            timesEquals(sums[i], s > 0 ? s : 1.);
        }
    }
    LOG.setCompleted(prog);
    if (LOG.isStatistics()) {
        LOG.statistics(new LongStatistic(KEY + ".iterations", iteration));
    }
    upper.destroy();
    lower.destroy();
    // Wrap result
    double totalvariance = 0.;
    Clustering<KMeansModel> result = new Clustering<>("k-Means Clustering", "kmeans-clustering");
    for (int i = 0; i < clusters.size(); i++) {
        DBIDs ids = clusters.get(i);
        if (ids.size() == 0) {
            continue;
        }
        double[] mean = means[i];
        double varsum = 0.;
        if (varstat) {
            DoubleVector mvec = DoubleVector.wrap(mean);
            for (DBIDIter it = ids.iter(); it.valid(); it.advance()) {
                varsum += distanceFunction.distance(mvec, relation.get(it));
            }
            totalvariance += varsum;
        }
        KMeansModel model = new KMeansModel(mean, varsum);
        result.addToplevelCluster(new Cluster<>(ids, model));
    }
    if (LOG.isStatistics() && varstat) {
        LOG.statistics(new DoubleStatistic(this.getClass().getName() + ".variance-sum", totalvariance));
    }
    return result;
}

Example 19 with WritableIntegerDataStore

use of de.lmu.ifi.dbs.elki.database.datastore.WritableIntegerDataStore in project elki by elki-project.

the class KMeansLloyd method run.

@Override
public Clustering<KMeansModel> run(Database database, Relation<V> relation) {
    if (relation.size() <= 0) {
        return new Clustering<>("k-Means Clustering", "kmeans-clustering");
    }
    // Choose initial means
    if (LOG.isStatistics()) {
        LOG.statistics(new StringStatistic(KEY + ".initialization", initializer.toString()));
    }
    double[][] means = initializer.chooseInitialMeans(database, relation, k, getDistanceFunction());
    // Setup cluster assignment store
    List<ModifiableDBIDs> clusters = new ArrayList<>();
    for (int i = 0; i < k; i++) {
        clusters.add(DBIDUtil.newHashSet((int) (relation.size() * 2. / k)));
    }
    WritableIntegerDataStore assignment = DataStoreUtil.makeIntegerStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, -1);
    double[] varsum = new double[k];
    IndefiniteProgress prog = LOG.isVerbose() ? new IndefiniteProgress("K-Means iteration", LOG) : null;
    DoubleStatistic varstat = LOG.isStatistics() ? new DoubleStatistic(this.getClass().getName() + ".variance-sum") : null;
    int iteration = 0;
    for (; maxiter <= 0 || iteration < maxiter; iteration++) {
        LOG.incrementProcessed(prog);
        boolean changed = assignToNearestCluster(relation, means, clusters, assignment, varsum);
        logVarstat(varstat, varsum);
        // Stop if no cluster assignment changed.
        if (!changed) {
            break;
        }
        // Recompute means.
        means = means(clusters, means, relation);
    }
    LOG.setCompleted(prog);
    if (LOG.isStatistics()) {
        LOG.statistics(new LongStatistic(KEY + ".iterations", iteration));
    }
    // Wrap result
    Clustering<KMeansModel> result = new Clustering<>("k-Means Clustering", "kmeans-clustering");
    for (int i = 0; i < clusters.size(); i++) {
        DBIDs ids = clusters.get(i);
        if (ids.size() == 0) {
            continue;
        }
        KMeansModel model = new KMeansModel(means[i], varsum[i]);
        result.addToplevelCluster(new Cluster<>(ids, model));
    }
    return result;
}

Example 20 with WritableIntegerDataStore

use of de.lmu.ifi.dbs.elki.database.datastore.WritableIntegerDataStore in project elki by elki-project.

the class KMeansMinusMinus method run.

@Override
public Clustering<KMeansModel> run(Database database, Relation<V> relation) {
    if (relation.size() <= 0) {
        return new Clustering<>("k-Means Clustering", "kmeans-clustering");
    }
    // Choose initial means
    if (LOG.isStatistics()) {
        LOG.statistics(new StringStatistic(KEY + ".initialization", initializer.toString()));
    }
    // Intialisieren der means
    double[][] means = initializer.chooseInitialMeans(database, relation, k, getDistanceFunction());
    // initialisieren vom Heap
    final int heapsize = (int) (rate < 1. ? Math.ceil(relation.size() * rate) : rate);
    DoubleMinHeap minHeap = new DoubleMinHeap(heapsize);
    // Setup cluster assignment store
    List<ModifiableDoubleDBIDList> clusters = new ArrayList<>();
    for (int i = 0; i < k; i++) {
        clusters.add(DBIDUtil.newDistanceDBIDList((int) (relation.size() * 2. / k)));
    }
    WritableIntegerDataStore assignment = DataStoreUtil.makeIntegerStorage(relation.getDBIDs(), DataStoreFactory.HINT_TEMP | DataStoreFactory.HINT_HOT, -1);
    double[] varsum = new double[k];
    IndefiniteProgress prog = LOG.isVerbose() ? new IndefiniteProgress("K-Means iteration", LOG) : null;
    DoubleStatistic varstat = new DoubleStatistic(this.getClass().getName() + ".variance-sum");
    // Otherwise, the vartotal break below will fail!
    assert (varstat != null);
    int iteration = 0;
    double prevvartotal = Double.POSITIVE_INFINITY;
    for (; maxiter <= 0 || iteration < maxiter; iteration++) {
        minHeap.clear();
        for (int i = 0; i < k; i++) {
            clusters.get(i).clear();
        }
        LOG.incrementProcessed(prog);
        boolean changed = assignToNearestCluster(relation, means, clusters, assignment, varsum, minHeap, heapsize);
        double vartotal = logVarstat(varstat, varsum);
        // than the previous value.
        if (!changed || vartotal > prevvartotal) {
            break;
        }
        prevvartotal = vartotal;
        // Recompute means.
        means = meansWithTreshhold(clusters, means, relation, heapsize > 0 ? minHeap.peek() : Double.POSITIVE_INFINITY);
    }
    // create noisecluster if wanted
    ModifiableDoubleDBIDList noiseids = null;
    if (noiseFlag && heapsize > 0) {
        clusters.add(noiseids = DBIDUtil.newDistanceDBIDList((int) (relation.size() * 2. / k)));
        double tresh = minHeap.peek();
        for (int i = 0; i < k; i++) {
            for (DoubleDBIDListMIter it = clusters.get(i).iter(); it.valid(); it.advance()) {
                final double dist = it.doubleValue();
                // Add to the noise cluster:
                if (dist >= tresh) {
                    noiseids.add(dist, it);
                    assignment.putInt(it, k);
                    it.remove();
                }
            }
        }
    }
    LOG.setCompleted(prog);
    if (LOG.isStatistics()) {
        LOG.statistics(new LongStatistic(KEY + ".iterations", iteration));
    }
    // Wrap result
    Clustering<KMeansModel> result = new Clustering<>("k-Means Clustering", "kmeans-clustering");
    for (int i = 0; i < k; i++) {
        DBIDs ids = clusters.get(i);
        if (ids.size() == 0) {
            continue;
        }
        KMeansModel model = new KMeansModel(means[i], varsum[i]);
        result.addToplevelCluster(new Cluster<>(ids, model));
    }
    // Noise Cluster
    if (noiseFlag) {
        KMeansModel model = new KMeansModel(null, 0);
        DBIDs ids = noiseids;
        if (ids.size() == 0) {
            return result;
        }
        result.addToplevelCluster(new Cluster<>(ids, true, model));
    }
    return result;
}