Examples with GaussianClusterDataSource org.tribuo.clustering.example.GaussianClusterDataSource used on opensource projects

Example 1 with GaussianClusterDataSource

use of org.tribuo.clustering.example.GaussianClusterDataSource in project tribuo by oracle.

the class TestHdbscan method runEvaluation.

public static void runEvaluation(HdbscanTrainer trainer) {
    DataSource<ClusterID> gaussianSource = new GaussianClusterDataSource(1000, 1L);
    TrainTestSplitter<ClusterID> splitter = new TrainTestSplitter<>(gaussianSource, 0.7f, 2L);
    Dataset<ClusterID> trainData = new MutableDataset<>(splitter.getTrain());
    Dataset<ClusterID> testData = new MutableDataset<>(splitter.getTest());
    ClusteringEvaluator eval = new ClusteringEvaluator();
    HdbscanModel model = trainer.train(trainData);
    // Test serialization
    Helpers.testModelSerialization(model, ClusterID.class);
    ClusteringEvaluation trainEvaluation = eval.evaluate(model, trainData);
    assertFalse(Double.isNaN(trainEvaluation.adjustedMI()));
    assertFalse(Double.isNaN(trainEvaluation.normalizedMI()));
    ClusteringEvaluation testEvaluation = eval.evaluate(model, testData);
    assertFalse(Double.isNaN(testEvaluation.adjustedMI()));
    assertFalse(Double.isNaN(testEvaluation.normalizedMI()));
}

Example 2 with GaussianClusterDataSource

use of org.tribuo.clustering.example.GaussianClusterDataSource in project tribuo by oracle.

the class TestKMeans method runEvaluation.

public static void runEvaluation(KMeansTrainer trainer) {
    Dataset<ClusterID> data = new MutableDataset<>(new GaussianClusterDataSource(500, 1L));
    Dataset<ClusterID> test = ClusteringDataGenerator.gaussianClusters(500, 2L);
    ClusteringEvaluator eval = new ClusteringEvaluator();
    KMeansModel model = trainer.train(data);
    ClusteringEvaluation trainEvaluation = eval.evaluate(model, data);
    assertFalse(Double.isNaN(trainEvaluation.adjustedMI()));
    assertFalse(Double.isNaN(trainEvaluation.normalizedMI()));
    ClusteringEvaluation testEvaluation = eval.evaluate(model, test);
    assertFalse(Double.isNaN(testEvaluation.adjustedMI()));
    assertFalse(Double.isNaN(testEvaluation.normalizedMI()));
}

Example 3 with GaussianClusterDataSource

use of org.tribuo.clustering.example.GaussianClusterDataSource in project tribuo by oracle.

the class TestHdbscan method runBasicTrainPredict.

public static void runBasicTrainPredict(HdbscanTrainer trainer) {
    DataSource<ClusterID> gaussianSource = new GaussianClusterDataSource(1000, 1L);
    TrainTestSplitter<ClusterID> splitter = new TrainTestSplitter<>(gaussianSource, 0.8f, 2L);
    Dataset<ClusterID> trainData = new MutableDataset<>(splitter.getTrain());
    Dataset<ClusterID> testData = new MutableDataset<>(splitter.getTest());
    HdbscanModel model = trainer.train(trainData);
    for (HdbscanTrainer.ClusterExemplar e : model.getClusterExemplars()) {
        assertTrue(e.getMaxDistToEdge() > 0.0);
    }
    List<Integer> clusterLabels = model.getClusterLabels();
    List<Double> outlierScores = model.getOutlierScores();
    List<Pair<Integer, List<Feature>>> exemplarLists = model.getClusters();
    List<HdbscanTrainer.ClusterExemplar> exemplars = model.getClusterExemplars();
    assertEquals(exemplars.size(), exemplarLists.size());
    // Check there's at least one exemplar per label
    Set<Integer> exemplarLabels = exemplarLists.stream().map(Pair::getA).collect(Collectors.toSet());
    Set<Integer> clusterLabelSet = new HashSet<>(clusterLabels);
    // Remove the noise label
    clusterLabelSet.remove(Integer.valueOf(0));
    assertEquals(exemplarLabels, clusterLabelSet);
    for (int i = 0; i < exemplars.size(); i++) {
        HdbscanTrainer.ClusterExemplar e = exemplars.get(i);
        Pair<Integer, List<Feature>> p = exemplarLists.get(i);
        assertEquals(model.getFeatureIDMap().size(), e.getFeatures().size());
        assertEquals(p.getB().size(), e.getFeatures().size());
        SGDVector otherFeatures = DenseVector.createDenseVector(new ArrayExample<>(trainData.getOutputFactory().getUnknownOutput(), p.getB()), model.getFeatureIDMap(), false);
        assertEquals(otherFeatures, e.getFeatures());
    }
    int[] expectedIntClusterLabels = { 4, 3, 4, 5, 3, 5, 3, 4, 3, 4, 5, 5, 3, 4, 4, 0, 3, 4, 0, 5, 5, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 0, 4, 5, 3, 5, 3, 4, 3, 4, 4, 3, 0, 5, 0, 4, 4, 4, 4, 4, 5, 4, 3, 4, 4, 4, 4, 4, 5, 3, 4, 3, 5, 3, 4, 5, 3, 4, 0, 5, 4, 4, 4, 4, 4, 5, 4, 4, 4, 4, 4, 5, 3, 4, 4, 3, 4, 3, 5, 5, 0, 5, 4, 4, 3, 5, 5, 4, 5, 5, 3, 5, 4, 4, 3, 5, 4, 5, 5, 5, 4, 4, 5, 5, 3, 5, 4, 4, 3, 5, 5, 3, 5, 4, 4, 5, 5, 5, 3, 5, 4, 5, 3, 4, 3, 5, 4, 4, 3, 3, 5, 4, 4, 5, 5, 4, 3, 4, 5, 4, 5, 4, 3, 3, 3, 4, 5, 4, 5, 5, 3, 4, 3, 3, 4, 5, 3, 5, 5, 5, 5, 5, 4, 4, 3, 4, 5, 5, 4, 4, 3, 4, 3, 4, 5, 4, 4, 5, 4, 3, 3, 0, 3, 5, 5, 3, 3, 3, 4, 3, 3, 5, 5, 5, 5, 3, 5, 5, 3, 5, 3, 4, 5, 3, 3, 3, 4, 4, 3, 3, 3, 5, 3, 4, 5, 3, 5, 5, 5, 3, 5, 3, 5, 4, 5, 4, 4, 5, 5, 5, 3, 5, 4, 5, 5, 4, 4, 4, 5, 4, 5, 4, 3, 3, 4, 5, 4, 4, 3, 3, 3, 4, 5, 4, 4, 4, 4, 5, 4, 4, 4, 5, 3, 5, 4, 5, 3, 5, 3, 5, 4, 4, 0, 4, 4, 5, 3, 4, 5, 5, 0, 5, 4, 5, 3, 4, 3, 5, 5, 4, 5, 5, 5, 5, 5, 5, 3, 5, 4, 3, 3, 5, 3, 4, 5, 4, 3, 5, 4, 3, 3, 3, 5, 4, 5, 4, 5, 5, 4, 3, 5, 4, 5, 4, 5, 4, 3, 4, 5, 4, 4, 5, 5, 5, 3, 4, 5, 4, 0, 3, 5, 3, 4, 3, 3, 5, 5, 5, 4, 4, 3, 3, 4, 3, 5, 3, 3, 4, 3, 5, 3, 4, 5, 4, 4, 3, 4, 4, 3, 3, 5, 4, 4, 5, 3, 5, 3, 3, 4, 5, 3, 4, 5, 5, 4, 4, 4, 5, 5, 5, 5, 3, 3, 4, 4, 4, 4, 4, 3, 5, 4, 3, 4, 4, 5, 3, 5, 3, 4, 5, 4, 4, 5, 3, 4, 4, 4, 5, 5, 4, 5, 0, 4, 5, 3, 4, 5, 4, 4, 4, 5, 4, 4, 4, 0, 3, 4, 5, 5, 4, 4, 3, 3, 4, 3, 3, 4, 5, 5, 4, 3, 5, 4, 4, 4, 4, 5, 4, 4, 3, 4, 5, 5, 4, 3, 4, 5, 4, 3, 5, 5, 5, 3, 4, 4, 4, 4, 4, 4, 5, 3, 3, 3, 5, 5, 4, 5, 3, 5, 3, 5, 4, 5, 3, 4, 5, 4, 3, 5, 4, 4, 5, 5, 0, 3, 3, 5, 5, 3, 0, 5, 5, 5, 5, 3, 4, 5, 4, 3, 3, 4, 5, 4, 4, 0, 5, 3, 4, 4, 4, 4, 5, 5, 5, 3, 5, 4, 3, 3, 5, 3, 4, 3, 5, 3, 3, 4, 3, 5, 4, 3, 4, 3, 0, 4, 5, 5, 5, 3, 4, 3, 5, 5, 4, 5, 4, 4, 4, 5, 4, 3, 4, 3, 4, 5, 3, 5, 4, 5, 3, 0, 4, 0, 4, 3, 3, 4, 3, 0, 3, 3, 3, 3, 4, 4, 5, 3, 3, 5, 4, 4, 4, 5, 5, 5, 3, 3, 4, 4, 3, 4, 5, 3, 4, 4, 5, 3, 4, 4, 4, 3, 4, 4, 4, 5, 4, 4, 5, 5, 5, 4, 4, 4, 5, 5, 5, 5, 4, 3, 4, 3, 3, 3, 4, 4, 5, 4, 5, 4, 4, 4, 4, 4, 5, 4, 5, 5, 5, 4, 3, 5, 3, 5, 4, 5, 4, 4, 5, 0, 5, 3, 4, 5, 4, 4, 5, 3, 4, 4, 3, 5, 4, 4, 4, 5, 3, 3, 4, 4, 5, 5, 5, 3, 4, 3, 4, 5, 5, 4, 4, 3, 3, 4, 4, 5, 5, 5, 3, 4, 3, 4, 4, 4, 5, 5, 5, 0, 4, 5, 5, 3, 3, 4, 5, 4, 3, 3, 4, 3, 4, 5, 4, 3, 4, 5, 5, 3, 3, 4, 4, 3, 3, 5, 4, 5, 3, 4, 5, 4, 3, 3, 4, 5, 5, 5, 3, 3, 4, 4, 5, 5, 5, 4, 5, 5, 5, 4, 4, 4, 5, 4, 5, 5, 3, 3, 4, 4, 3, 5, 5, 3, 3, 4, 4, 5, 3, 3, 3 };
    List<Integer> expectedClusterLabels = Arrays.stream(expectedIntClusterLabels).boxed().collect(Collectors.toList());
    double[] expectedDoubleOutlierScores = { 0.46676776260759345, 0.2743698754772864, 0.7559982720268424, 0.8501840034553623, 0.49318092730464635, 0.13138938738160744, 0.4713199767058086, 0.6252876350317327, 0.5993132028604171, 0.5099794170903283, 0.34739656697344323, 0.7877610766946352, 0.6725050057122981, 0.0, 0.3443411864540462, 0.942517632028674, 0.49375727602750374, 0.0, 0.8895331356424256, 0.6324670047095703, 0.42882347542687815, 0.49318092730464635, 0.691903096844513, 0.6380593801053474, 0.2406826282408977, 0.0, 0.6968734399959293, 0.3610993140443196, 0.5535004360403812, 0.6096176323143576, 0.0, 0.0, 0.3913407463849664, 0.9519927727728552, 0.0, 0.5393032152890598, 0.6503011262262826, 0.1433842216333847, 0.49506479112319557, 0.5709634323345956, 0.4563315958116082, 0.44618653226418115, 0.44814977073944906, 0.0, 0.9074703755075781, 0.7291450269088865, 0.9484293814844095, 0.0, 0.3705649211930552, 0.3480591862782948, 0.0, 0.7517459138118392, 0.5690934599956823, 0.6502288567686347, 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0.87242048722334, 0.45114165259596173, 0.4267483686744079, 0.4459420844294427, 0.4979058181573891, 0.6687168793513216, 0.6725039248778191, 0.5898326395194236, 0.3786565035491881, 0.49270476278526754, 0.6845482002842296, 0.5312504911095062, 0.0, 0.14012035981759297, 0.0, 0.13260183495549782, 0.0, 0.1114322036219213, 0.0, 0.22354672645501728, 0.4437011486963448, 0.32454746691008174, 0.2129513856466727, 0.7322362660260833, 0.6428127919521037, 0.7299583115950901, 0.3805190861517057, 0.5942874634038553, 0.32454746691008174, 0.48232688782776534, 0.0, 0.5079723021903902, 0.426792378877326, 0.0, 0.0, 0.0, 0.46129103283467177, 0.42701054653212456, 0.05399443648683844, 0.32963857165340793, 0.4011402980489066, 0.49884143112507695, 0.6742625110529403, 0.7000703820982973, 0.0059186060290421505, 0.3935328253717545, 0.4982012977088105, 0.46129103283467177, 0.1884812300740285, 0.0, 0.0, 0.0, 0.7193239335926954, 0.20322704801088176, 0.6147177259392704, 0.6386340973679066, 0.4605406727289195, 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0.4365915265432285, 0.3819107449024405, 0.6726949923291138, 0.4011402980489066, 0.3935328253717545, 0.25196441733320185, 0.881350459326978, 0.7349084526542964, 0.6047151306067444, 0.0, 0.20977384411280398, 0.4679944192020826, 0.6698566773200858, 0.0, 0.2959490414383299, 0.7932391110064335, 0.622319399561805, 0.8015596575831598, 0.39998141687078625, 0.4574247863501638, 0.17981462714349217, 0.3717035453553832, 0.6667041054756846, 0.553583549266421, 0.3443411864540462, 0.5433733027536815, 0.5148600399821457, 0.6968734399959293, 0.580828106385362, 0.39422048008001054, 0.5733419619457072, 0.20675485588245224, 0.35152584520905694, 0.5031988755530088, 0.5467067205040299, 0.0, 0.4414501866925844, 0.0, 0.49147375734502297, 0.7537969758806241, 0.05399443648683844, 0.0, 0.8130406304754614, 0.3893369731046228, 0.08857940056865066, 0.12319636014473168, 0.47827834758956966, 0.46196635668966657, 0.36779495204329093, 0.3139968403491683, 0.455185529426422, 0.0, 0.0, 0.23496850036493677, 0.31517820690329956, 0.4727343218057717, 0.0, 0.6980083836215727, 0.7249879488732429, 0.5404377149880093, 0.36704925127119226, 0.0, 0.0, 0.7939891118341219, 0.858959809289114, 0.6243592907663799, 0.0, 0.7193239335926954, 0.4487733002302773, 0.7295180327424304, 0.29131593514163934, 0.13649363353017852, 0.3562234122805952, 0.8627287953882614, 0.0, 0.8959448055905569, 0.697568664523059, 0.6847290646834571, 0.3811096272517188, 0.3921958171277884, 0.0, 0.9138808799654548, 0.8373253417127464, 0.03806887171267048, 0.0, 0.32210719489953354, 0.8993257360128568, 0.5733419619457072, 0.5298320190732928, 0.5406291933185892, 0.4936169024186612, 0.0, 0.5676673482591083, 0.797644954156777, 0.25196441733320185, 0.7517459138118392, 0.8804787122327714, 0.4437951161132294, 0.517719961495386, 0.6736118674798475, 0.0, 0.3869796117891282, 0.0, 0.0, 0.11368083044659938, 0.49476284568662654, 0.0, 0.3990149169995906, 0.5497567156841348, 0.49755861260331113, 0.5431034895139442, 0.4849594616738354, 0.634359329239355, 0.6102777431117171, 0.5993132028604171, 0.029110696378704337, 0.0041352367783195065, 0.5455357591968216, 0.614637828267818, 0.3872053944517456, 0.8774911291582113, 0.0, 0.515011903882065, 0.5653271117366987, 0.6725050057122981, 0.8828338637775253, 0.0, 0.025305516244955806, 0.1153305490422244, 0.1189992879103362, 0.0, 0.4623766305089029, 0.4972070270025144, 0.0, 0.19162317880112645, 0.0, 0.0, 0.46322024301962184, 0.3564486209165576, 0.13221917122566706, 0.8074269371585792, 0.8086158459470157, 0.45215040851165234, 0.7250991976553953, 0.7104042746619621, 0.0, 0.7564957221431223, 0.6678218872757002, 0.49889559243077775, 0.6362040007967809, 0.8504597507708, 0.45590549902450317, 0.9009290923658844, 0.2935311018632297, 0.8837545475640602, 0.5878793983623484, 0.0, 0.7173151984840158, 0.6299022163181591, 0.0, 0.9104797902272275, 0.5661891151536319, 0.6891663882134245, 0.5072740714951296, 0.594386514812078, 0.5731856857918096, 0.0, 0.19553532551745423, 0.6285842416556849, 0.5404377149880093, 0.029110696378704337, 0.47054972913556337, 0.5475804999462761, 0.3805190861517057, 0.7966719729940861, 0.7555806975782535, 0.010061020299601542, 0.6333336380718192, 0.8917306933911169, 0.2935311018632297, 0.07406510775737263, 0.45215040851165234, 0.18097453586463574, 0.0, 0.08132701192855352, 0.4366734759781771, 0.3564486209165576, 0.7310554814333057, 0.9204338265893961, 0.08471089907230389, 0.5696608258449514, 0.6922091225884852, 0.6279823624589616, 0.0, 0.3935328253717545, 0.0, 0.7413065729103836, 0.680500524047025, 0.6769166895418774, 0.5002418053594004, 0.0, 0.39649970945668, 0.0, 0.25196441733320185, 0.22794789542087268, 0.535253084286895, 0.797644954156777, 0.6736140765300983, 0.6390655334750306, 0.4613651254516825, 0.7655684232457723, 0.0, 0.3872053944517456, 0.6047587670112572, 0.7216841523191753, 0.6581024749535422, 0.2957896263676023, 0.0, 0.0, 0.1358560783357583, 0.3443411864540462, 0.32972277729175326, 0.7412343863236006, 0.7137328127320526, 0.4807164899348386, 0.5816339146436611, 0.0, 0.39649970945668, 0.6554079940503144, 0.32081918857815017, 0.6250251993493536, 0.7099539309159079, 0.8460951407432118, 0.7210253357230385, 0.07792093768838082, 0.0, 0.8460951407432118, 0.617166909604469, 0.0, 0.17820178754345894, 0.9208897313509731, 0.1494984069468719, 0.6800023209762943, 0.0, 0.4314997931110507, 0.3818456558681199, 0.3559937076353532, 0.3573443810325546, 0.0, 0.0, 0.4108575710450929, 0.5404377149880093, 0.3107705511500243, 0.34467641363479895, 0.6588136178954032, 0.0, 0.7124877485819077, 0.4271754772995946, 0.6352932475152177, 0.6358395247009956, 0.6157498971205291, 0.2129513856466727, 0.3300832680463701, 0.3922526981839124, 0.0, 0.0, 0.5993702342757096, 0.6044080434255494, 0.0, 0.4254461589520476, 0.505472792649011, 0.5052779575119282, 0.0, 0.7683354875196753, 0.41622494718112346, 0.28903657152106876, 0.34739656697344323, 0.0, 0.22045718710077167, 0.850119563971185, 0.53811553498556, 0.0, 0.5899675992703288, 0.3935328253717545, 0.5352299710205285, 0.5928945724077263, 0.4252236114436363, 0.7329731025102499, 0.9228363488631618, 0.10329887928234749, 0.7193239335926954, 0.025305516244955806, 0.6827262685358917, 0.5204591459253809, 0.31848573305331973, 0.8307996083812397, 0.08118104079195632, 0.5490352427566516, 0.5779985937481655, 0.7740622259507297, 0.7718601671125394, 0.4945464442491214, 0.818070291907619, 0.2935311018632297, 0.5633646723184442, 0.0, 0.025305516244955806, 0.803889349277999, 0.04556290658626683, 0.6682951293623198, 0.0, 0.16135790337112854, 0.2656863426127436, 0.5856638908845988, 0.6415288134310423, 0.0, 0.5826267384294974, 0.8404883382141526, 0.7401049925075427, 0.8552831593916908, 0.5688163749124342, 0.0, 0.0, 0.4108575710450929, 0.0, 0.7858019964605708, 0.23346803074513822, 0.670754636973998, 0.07860444563433111, 0.0, 0.7286247704137883, 0.4334354337345858, 0.49880706067428204, 0.0, 0.5696608258449514, 0.29875909495688924, 0.5298320190732928, 0.18191542983970188, 0.3769853117713595, 0.43820864595574294, 0.7169533729847726, 0.7224900394386976, 0.41293235299928155, 0.5819766000229493, 0.4295586290786334, 0.936944591291591, 0.5404377149880093, 0.3935328253717545, 0.31848573305331973, 0.0, 0.7036693377297398, 0.02065034905954999, 0.4224925490810527, 0.11216184489956871, 0.42215466113858024, 0.0, 0.39649970945668, 0.4396553712320629, 0.8400030552287128, 0.6735267975566936 };
    List<Double> expectedOutlierScores = Arrays.stream(expectedDoubleOutlierScores).boxed().collect(Collectors.toList());
    assertEquals(expectedClusterLabels, clusterLabels);
    assertEquals(expectedOutlierScores, outlierScores);
    List<Prediction<ClusterID>> predictions = model.predict(testData);
    int i = 0;
    int[] actualLabelPredictions = new int[testData.size()];
    double[] actualOutlierScorePredictions = new double[testData.size()];
    for (Prediction<ClusterID> pred : predictions) {
        actualLabelPredictions[i] = pred.getOutput().getID();
        actualOutlierScorePredictions[i] = pred.getOutput().getScore();
        i++;
    }
    int[] expectedLabelPredictions = { 4, 5, 3, 5, 5, 3, 5, 4, 5, 3, 5, 5, 4, 4, 4, 5, 3, 4, 4, 3, 3, 5, 4, 5, 4, 5, 3, 3, 4, 5, 4, 4, 5, 3, 4, 5, 4, 4, 5, 5, 3, 5, 5, 5, 4, 5, 3, 4, 4, 5, 5, 5, 3, 3, 5, 4, 3, 5, 5, 5, 4, 5, 4, 5, 3, 4, 4, 3, 3, 3, 4, 5, 5, 5, 5, 3, 4, 5, 3, 5, 5, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 3, 4, 3, 3, 5, 3, 5, 5, 4, 4, 4, 4, 3, 3, 4, 4, 4, 4, 4, 3, 3, 5, 5, 5, 4, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 5, 5, 4, 5, 4, 4, 3, 5, 4, 5, 5, 3, 4, 5, 5, 4, 5, 4, 4, 3, 3, 5, 5, 5, 5, 4, 4, 5, 4, 4, 4, 4, 5, 3, 5, 5, 5, 3, 4, 5, 4, 4, 4, 4, 4, 4, 5, 5, 3, 4, 3, 5, 5, 5, 4, 5, 5, 5, 4, 4, 3, 4, 5, 4, 4, 5, 5, 5, 5, 4, 5, 5, 5, 5 };
    double[] expectedOutlierScorePredictions = { 0.08118104079195632, 0.0, 0.04556290658626683, 0.010061020299601542, 0.029110696378704337, 0.08132701192855352, 0.010061020299601542, 0.07838284850471278, 0.025305516244955806, 0.08132701192855352, 0.03806887171267048, 0.02065034905954999, 0.08118104079195632, 0.0059186060290421505, 0.08118104079195632, 0.0, 0.08132701192855352, 0.0059186060290421505, 0.08118104079195632, 0.0041352367783195065, 0.08132701192855352, 0.02065034905954999, 0.08471089907230389, 0.025305516244955806, 0.0, 0.0, 0.0, 0.0, 0.08118104079195632, 0.06314528545928999, 0.07838284850471278, 0.0059186060290421505, 0.010061020299601542, 0.0041352367783195065, 0.0059186060290421505, 0.010061020299601542, 0.08118104079195632, 0.08118104079195632, 0.029110696378704337, 0.010061020299601542, 0.08132701192855352, 0.03806887171267048, 0.03806887171267048, 0.029110696378704337, 0.07406510775737263, 0.02065034905954999, 0.04556290658626683, 0.07838284850471278, 0.08118104079195632, 0.0, 0.0, 0.02065034905954999, 0.0041352367783195065, 0.0, 0.03806887171267048, 0.08118104079195632, 0.0041352367783195065, 0.03806887171267048, 0.02065034905954999, 0.02065034905954999, 0.00509618317782945, 0.06314528545928999, 0.0059186060290421505, 0.029110696378704337, 0.07860444563433111, 0.08118104079195632, 0.08471089907230389, 0.0041352367783195065, 0.0, 0.07860444563433111, 0.07838284850471278, 0.029110696378704337, 0.010061020299601542, 0.0, 0.06314528545928999, 0.0041352367783195065, 0.08118104079195632, 0.029110696378704337, 0.0, 0.02065034905954999, 0.06314528545928999, 0.0, 0.08471089907230389, 0.00509618317782945, 0.08212338693601973, 0.08471089907230389, 0.00509618317782945, 0.08118104079195632, 0.08118104079195632, 0.0, 0.025305516244955806, 0.0, 0.08118104079195632, 0.0041352367783195065, 0.0, 0.06314528545928999, 0.08132701192855352, 0.025305516244955806, 0.029110696378704337, 0.07838284850471278, 0.08118104079195632, 0.0, 0.053392009649182115, 0.05399443648683844, 0.07860444563433111, 0.08118104079195632, 0.08471089907230389, 0.08212338693601973, 0.08118104079195632, 0.0, 0.04556290658626683, 0.08132701192855352, 0.03806887171267048, 0.0, 0.0, 0.0, 0.08118104079195632, 0.029110696378704337, 0.0059186060290421505, 0.02065034905954999, 0.08118104079195632, 0.02065034905954999, 0.053392009649182115, 0.010061020299601542, 0.07838284850471278, 0.02065034905954999, 0.010061020299601542, 0.0, 0.08118104079195632, 0.029110696378704337, 0.0059186060290421505, 0.0, 0.08132701192855352, 0.025305516244955806, 0.0059186060290421505, 0.029110696378704337, 0.029110696378704337, 0.0, 0.00509618317782945, 0.025305516244955806, 0.010061020299601542, 0.08118104079195632, 0.029110696378704337, 0.08118104079195632, 0.0059186060290421505, 0.08132701192855352, 0.0, 0.06314528545928999, 0.029110696378704337, 0.029110696378704337, 0.029110696378704337, 0.0059186060290421505, 0.08212338693601973, 0.02065034905954999, 0.08118104079195632, 0.0059186060290421505, 0.0059186060290421505, 0.00509618317782945, 0.0, 0.08132701192855352, 0.025305516244955806, 0.025305516244955806, 0.02065034905954999, 0.0, 0.08118104079195632, 0.03806887171267048, 0.08471089907230389, 0.07838284850471278, 0.07838284850471278, 0.08118104079195632, 0.07838284850471278, 0.07838284850471278, 0.02065034905954999, 0.029110696378704337, 0.07860444563433111, 0.07838284850471278, 0.08132701192855352, 0.03806887171267048, 0.025305516244955806, 0.010061020299601542, 0.08471089907230389, 0.029110696378704337, 0.025305516244955806, 0.03806887171267048, 0.08471089907230389, 0.0, 0.07860444563433111, 0.08212338693601973, 0.029110696378704337, 0.08118104079195632, 0.08118104079195632, 0.06314528545928999, 0.010061020299601542, 0.010061020299601542, 0.029110696378704337, 0.07838284850471278, 0.025305516244955806, 0.010061020299601542, 0.010061020299601542, 0.06314528545928999 };
    assertArrayEquals(expectedLabelPredictions, actualLabelPredictions);
    assertArrayEquals(expectedOutlierScorePredictions, actualOutlierScorePredictions);
}