Examples with DoubleDBIDListIter de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDListIter used on opensource projects

Example 26 with DoubleDBIDListIter

use of de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDListIter in project elki by elki-project.

the class RdKNNTree method reverseKNNQuery.

public DoubleDBIDList reverseKNNQuery(DBID oid, int k, SpatialPrimitiveDistanceFunction<? super O> distanceFunction, KNNQuery<O> knnQuery) {
    checkDistanceFunction(distanceFunction);
    if (k > settings.k_max) {
        throw new IllegalArgumentException("Parameter k is not supported, k > k_max: " + k + " > " + settings.k_max);
    }
    // get candidates
    ModifiableDoubleDBIDList candidates = DBIDUtil.newDistanceDBIDList();
    doReverseKNN(getRoot(), oid, candidates);
    if (k == settings.k_max) {
        candidates.sort();
        return candidates;
    }
    // refinement of candidates, if k < k_max
    ArrayModifiableDBIDs candidateIDs = DBIDUtil.newArray(candidates);
    candidateIDs.sort();
    List<? extends KNNList> knnLists = knnQuery.getKNNForBulkDBIDs(candidateIDs, k);
    ModifiableDoubleDBIDList result = DBIDUtil.newDistanceDBIDList();
    int i = 0;
    for (DBIDIter iter = candidateIDs.iter(); iter.valid(); iter.advance(), i++) {
        for (DoubleDBIDListIter qr = knnLists.get(i).iter(); qr.valid(); qr.advance()) {
            if (DBIDUtil.equal(oid, qr)) {
                result.add(qr.doubleValue(), iter);
                break;
            }
        }
    }
    result.sort();
    return result;
}

Example 27 with DoubleDBIDListIter

use of de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDListIter in project elki by elki-project.

the class AbstractIndexStructureTest method testExactCosine.

/**
 * Actual test routine, for cosine distance
 *
 * @param inputparams
 */
protected void testExactCosine(ListParameterization inputparams, Class<?> expectKNNQuery, Class<?> expectRangeQuery) {
    // Use a fixed DBID - historically, we used 1 indexed - to reduce random
    // variation in results due to different hash codes everywhere.
    inputparams.addParameter(AbstractDatabaseConnection.Parameterizer.FILTERS_ID, new FixedDBIDsFilter(1));
    Database db = AbstractSimpleAlgorithmTest.makeSimpleDatabase(dataset, shoulds, inputparams);
    Relation<DoubleVector> rep = db.getRelation(TypeUtil.DOUBLE_VECTOR_FIELD);
    DistanceQuery<DoubleVector> dist = db.getDistanceQuery(rep, CosineDistanceFunction.STATIC);
    if (expectKNNQuery != null) {
        // get the 10 next neighbors
        DoubleVector dv = DoubleVector.wrap(querypoint);
        KNNQuery<DoubleVector> knnq = db.getKNNQuery(dist, k);
        assertTrue("Returned knn query is not of expected class: expected " + expectKNNQuery + " got " + knnq.getClass(), expectKNNQuery.isAssignableFrom(knnq.getClass()));
        KNNList ids = knnq.getKNNForObject(dv, k);
        assertEquals("Result size does not match expectation!", cosshouldd.length, ids.size());
        // verify that the neighbors match.
        int i = 0;
        for (DoubleDBIDListIter res = ids.iter(); res.valid(); res.advance(), i++) {
            // Verify distance
            assertEquals("Expected distance doesn't match.", cosshouldd[i], res.doubleValue(), 1e-15);
            // verify vector
            DoubleVector c = rep.get(res);
            DoubleVector c2 = DoubleVector.wrap(cosshouldc[i]);
            assertEquals("Expected vector doesn't match: " + c.toString(), 0.0, dist.distance(c, c2), 1e-15);
        }
    }
    if (expectRangeQuery != null) {
        // Do a range query
        DoubleVector dv = DoubleVector.wrap(querypoint);
        RangeQuery<DoubleVector> rangeq = db.getRangeQuery(dist, coseps);
        assertTrue("Returned range query is not of expected class: expected " + expectRangeQuery + " got " + rangeq.getClass(), expectRangeQuery.isAssignableFrom(rangeq.getClass()));
        DoubleDBIDList ids = rangeq.getRangeForObject(dv, coseps);
        assertEquals("Result size does not match expectation!", cosshouldd.length, ids.size());
        // verify that the neighbors match.
        int i = 0;
        for (DoubleDBIDListIter res = ids.iter(); res.valid(); res.advance(), i++) {
            // Verify distance
            assertEquals("Expected distance doesn't match.", cosshouldd[i], res.doubleValue(), 1e-15);
            // verify vector
            DoubleVector c = rep.get(res);
            DoubleVector c2 = DoubleVector.wrap(cosshouldc[i]);
            assertEquals("Expected vector doesn't match: " + c.toString(), 0.0, dist.distance(c, c2), 1e-15);
        }
    }
}

Example 28 with DoubleDBIDListIter

use of de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDListIter in project elki by elki-project.

the class AbstractIndexStructureTest method testExactEuclidean.

/**
 * Actual test routine.
 *
 * @param inputparams
 */
protected void testExactEuclidean(ListParameterization inputparams, Class<?> expectKNNQuery, Class<?> expectRangeQuery) {
    // Use a fixed DBID - historically, we used 1 indexed - to reduce random
    // variation in results due to different hash codes everywhere.
    inputparams.addParameter(AbstractDatabaseConnection.Parameterizer.FILTERS_ID, new FixedDBIDsFilter(1));
    Database db = AbstractSimpleAlgorithmTest.makeSimpleDatabase(dataset, shoulds, inputparams);
    Relation<DoubleVector> rep = db.getRelation(TypeUtil.DOUBLE_VECTOR_FIELD);
    DistanceQuery<DoubleVector> dist = db.getDistanceQuery(rep, EuclideanDistanceFunction.STATIC);
    if (expectKNNQuery != null) {
        // get the 10 next neighbors
        DoubleVector dv = DoubleVector.wrap(querypoint);
        KNNQuery<DoubleVector> knnq = db.getKNNQuery(dist, k);
        assertTrue("Returned knn query is not of expected class: expected " + expectKNNQuery + " got " + knnq.getClass(), expectKNNQuery.isAssignableFrom(knnq.getClass()));
        KNNList ids = knnq.getKNNForObject(dv, k);
        assertEquals("Result size does not match expectation!", shouldd.length, ids.size(), 1e-15);
        // verify that the neighbors match.
        int i = 0;
        for (DoubleDBIDListIter res = ids.iter(); res.valid(); res.advance(), i++) {
            // Verify distance
            assertEquals("Expected distance doesn't match.", shouldd[i], res.doubleValue(), 1e-6);
            // verify vector
            DoubleVector c = rep.get(res);
            DoubleVector c2 = DoubleVector.wrap(shouldc[i]);
            assertEquals("Expected vector doesn't match: " + c.toString(), 0.0, dist.distance(c, c2), 1e-15);
        }
    }
    if (expectRangeQuery != null) {
        // Do a range query
        DoubleVector dv = DoubleVector.wrap(querypoint);
        RangeQuery<DoubleVector> rangeq = db.getRangeQuery(dist, eps);
        assertTrue("Returned range query is not of expected class: expected " + expectRangeQuery + " got " + rangeq.getClass(), expectRangeQuery.isAssignableFrom(rangeq.getClass()));
        DoubleDBIDList ids = rangeq.getRangeForObject(dv, eps);
        assertEquals("Result size does not match expectation!", shouldd.length, ids.size(), 1e-15);
        // verify that the neighbors match.
        int i = 0;
        for (DoubleDBIDListIter res = ids.iter(); res.valid(); res.advance(), i++) {
            // Verify distance
            assertEquals("Expected distance doesn't match.", shouldd[i], res.doubleValue(), 1e-6);
            // verify vector
            DoubleVector c = rep.get(res);
            DoubleVector c2 = DoubleVector.wrap(shouldc[i]);
            assertEquals("Expected vector doesn't match: " + c.toString(), 0.0, dist.distance(c, c2), 1e-15);
        }
    }
}

Example 29 with DoubleDBIDListIter

use of de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDListIter in project elki by elki-project.

the class DistanceStddevOutlier method run.

/**
 * Run the outlier detection algorithm
 *
 * @param database Database to use
 * @param relation Relation to analyze
 * @return Outlier score result
 */
public OutlierResult run(Database database, Relation<O> relation) {
    // Get a nearest neighbor query on the relation.
    KNNQuery<O> knnq = QueryUtil.getKNNQuery(relation, getDistanceFunction(), k);
    // Output data storage
    WritableDoubleDataStore scores = DataStoreUtil.makeDoubleStorage(relation.getDBIDs(), DataStoreFactory.HINT_DB);
    // Track minimum and maximum scores
    DoubleMinMax minmax = new DoubleMinMax();
    // Iterate over all objects
    for (DBIDIter iter = relation.iterDBIDs(); iter.valid(); iter.advance()) {
        KNNList neighbors = knnq.getKNNForDBID(iter, k);
        // Aggregate distances
        MeanVariance mv = new MeanVariance();
        for (DoubleDBIDListIter neighbor = neighbors.iter(); neighbor.valid(); neighbor.advance()) {
            // Skip the object itself. The 0 is not very informative.
            if (DBIDUtil.equal(iter, neighbor)) {
                continue;
            }
            mv.put(neighbor.doubleValue());
        }
        // Store score
        scores.putDouble(iter, mv.getSampleStddev());
    }
    // Wrap the result in the standard containers
    // Actual min-max, theoretical min-max!
    OutlierScoreMeta meta = new BasicOutlierScoreMeta(minmax.getMin(), minmax.getMax(), 0, Double.POSITIVE_INFINITY);
    DoubleRelation rel = new MaterializedDoubleRelation(relation.getDBIDs(), "stddev-outlier", scores);
    return new OutlierResult(meta, rel);
}

Example 30 with DoubleDBIDListIter

use of de.lmu.ifi.dbs.elki.database.ids.DoubleDBIDListIter in project elki by elki-project.

the class KNNClassifier method classProbabilities.

public double[] classProbabilities(O instance, ArrayList<ClassLabel> labels) {
    int[] occurences = new int[labels.size()];
    KNNList query = knnq.getKNNForObject(instance, k);
    for (DoubleDBIDListIter neighbor = query.iter(); neighbor.valid(); neighbor.advance()) {
        int index = Collections.binarySearch(labels, labelrep.get(neighbor));
        if (index >= 0) {
            occurences[index]++;
        }
    }
    double[] distribution = new double[labels.size()];
    for (int i = 0; i < distribution.length; i++) {
        distribution[i] = ((double) occurences[i]) / (double) query.size();
    }
    return distribution;
}