Examples with DoubleVector de.lmu.ifi.dbs.elki.data.DoubleVector used on opensource projects

Example 51 with DoubleVector

use of de.lmu.ifi.dbs.elki.data.DoubleVector 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 52 with DoubleVector

use of de.lmu.ifi.dbs.elki.data.DoubleVector 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 53 with DoubleVector

use of de.lmu.ifi.dbs.elki.data.DoubleVector in project elki by elki-project.

the class SimpleKernelDensityLOFTest method testLDF.

@Test
public void testLDF() {
    Database db = makeSimpleDatabase(UNITTEST + "outlier-axis-subspaces-6d.ascii", 1345);
    OutlierResult result = // 
    new ELKIBuilder<SimpleKernelDensityLOF<DoubleVector>>(SimpleKernelDensityLOF.class).with(LOF.Parameterizer.K_ID, // 
    20).with(SimpleKernelDensityLOF.Parameterizer.KERNEL_ID, // 
    BiweightKernelDensityFunction.class).build().run(db);
    testAUC(db, "Noise", result, 0.87192156);
    testSingleScore(result, 1293, 12.271188);
}

Example 54 with DoubleVector

use of de.lmu.ifi.dbs.elki.data.DoubleVector in project elki by elki-project.

the class AggarwalYuEvolutionaryTest method testAggarwalYuEvolutionary.

@Test
public void testAggarwalYuEvolutionary() {
    Database db = makeSimpleDatabase(UNITTEST + "outlier-3d-3clusters.ascii", 960);
    OutlierResult result = // 
    new ELKIBuilder<AggarwalYuEvolutionary<DoubleVector>>(AggarwalYuEvolutionary.class).with(AggarwalYuEvolutionary.Parameterizer.K_ID, // 
    2).with(AggarwalYuEvolutionary.Parameterizer.PHI_ID, // 
    8).with(AggarwalYuEvolutionary.Parameterizer.M_ID, // 
    20).with(AggarwalYuEvolutionary.Parameterizer.SEED_ID, // 
    0).build().run(db);
    testAUC(db, "Noise", result, 0.653888888888);
    testSingleScore(result, 945, 0.0);
}

Example 55 with DoubleVector

use of de.lmu.ifi.dbs.elki.data.DoubleVector in project elki by elki-project.

the class SODTest method testSOD.

@Test
public void testSOD() {
    Database db = makeSimpleDatabase(UNITTEST + "outlier-axis-subspaces-6d.ascii", 1345);
    OutlierResult result = // 
    new ELKIBuilder<SOD<DoubleVector>>(SOD.class).with(SOD.Parameterizer.KNN_ID, // 
    25).with(SharedNearestNeighborPreprocessor.Factory.NUMBER_OF_NEIGHBORS_ID, // 
    19).build().run(db);
    testSingleScore(result, 1293, 1.5167500);
    testAUC(db, "Noise", result, 0.949131652);
}