@Override @Before public void setUp() { RandomUtils.useTestSeed(); }
@Override @Before public void setUp() { RandomUtils.useTestSeed(); }
@Override @Before public void setUp() { RandomUtils.useTestSeed(); }
@Before public void setUp() throws Exception { testTempDir = null; RandomUtils.useTestSeed(); }
@Before public void testSetUp() { RandomUtils.useTestSeed(); }
@Before public void setUp() { RandomUtils.useTestSeed(); }
@Override @Before public void setUp() { RandomUtils.useTestSeed(); }
@Test public void testSimpleDist() { RandomUtils.useTestSeed(); Empirical z = new Empirical(true, true, 3, 0, 1, 0.5, 2, 1, 3.0); List<Double> r = Lists.newArrayList(); for (int i = 0; i < 10001; i++) { r.add(z.sample()); } Collections.sort(r); assertEquals(2.0, r.get(5000), 0.15); }
@Test public void testBasicText() { RandomUtils.useTestSeed(); IndianBuffet<String> sampler = IndianBuffet.createTextDocumentSampler(30); Multiset<String> counts = HashMultiset.create(); int[] lengths = new int[100]; for (int i = 0; i < 30; i++) { final List<String> doc = sampler.sample(); lengths[doc.size()]++; for (String w : doc) { counts.add(w); } System.out.printf("%s\n", doc); } } }
public static void main(String[] args) throws Exception { RandomUtils.useTestSeed();
@Before public void setUp() throws Exception { testTempDir = null; RandomUtils.useTestSeed(); }
@Before public void setUp() { RandomUtils.useTestSeed(); syntheticData = DataUtils.sampleMultiNormalHypercube(NUM_DIMENSIONS, NUM_DATA_POINTS, 1.0e-4); }
@BeforeClass public static void setUp() { RandomUtils.useTestSeed(); syntheticData = DataUtils.sampleMultiNormalHypercube(NUM_DIMENSIONS, NUM_DATA_POINTS, DISTRIBUTION_RADIUS); }
@Before public void setUp() { RandomUtils.useTestSeed(); syntheticData = DataUtils.sampleMultiNormalHypercube(NUM_DIMENSIONS, NUM_DATA_POINTS); }
@Override @Before public void setUp() throws Exception { super.setUp(); RandomUtils.useTestSeed(); testTempDirPath = null; fs = null; }
static void train(Matrix input, Vector target, OnlineLearner lr) { RandomUtils.useTestSeed(); Random gen = RandomUtils.getRandom(); // train on samples in random order (but only one pass) for (int row : permute(gen, 60)) { lr.train((int) target.get(row), input.viewRow(row)); } lr.close(); }
@Test public void onlineAucRoundtrip() throws IOException { RandomUtils.useTestSeed(); OnlineAuc auc1 = new GlobalOnlineAuc(); Random gen = RandomUtils.getRandom(); for (int i = 0; i < 10000; i++) { auc1.addSample(0, gen.nextGaussian()); auc1.addSample(1, gen.nextGaussian() + 1); } assertEquals(0.76, auc1.auc(), 0.01); OnlineAuc auc3 = roundTrip(auc1, OnlineAuc.class); assertEquals(auc1.auc(), auc3.auc(), 0); for (int i = 0; i < 1000; i++) { auc1.addSample(0, gen.nextGaussian()); auc1.addSample(1, gen.nextGaussian() + 1); auc3.addSample(0, gen.nextGaussian()); auc3.addSample(1, gen.nextGaussian() + 1); } assertEquals(auc1.auc(), auc3.auc(), 0.01); }
@Parameterized.Parameters public static List<Object[]> generateData() { RandomUtils.useTestSeed(); Matrix dataPoints = multiNormalRandomData(NUM_DATA_POINTS, NUM_DIMENSIONS); return Arrays.asList(new Object[][]{ {new ProjectionSearch(new EuclideanDistanceMeasure(), NUM_PROJECTIONS, SEARCH_SIZE), dataPoints}, {new FastProjectionSearch(new EuclideanDistanceMeasure(), NUM_PROJECTIONS, SEARCH_SIZE), dataPoints}, {new LocalitySensitiveHashSearch(new EuclideanDistanceMeasure(), SEARCH_SIZE), dataPoints}, }); }
@Parameterized.Parameters public static List<Object[]> generateData() { RandomUtils.useTestSeed(); Matrix dataPoints = LumpyData.lumpyRandomData(NUM_DATA_POINTS, NUM_DIMENSIONS); Matrix queries = LumpyData.lumpyRandomData(NUM_QUERIES, NUM_DIMENSIONS); DistanceMeasure distanceMeasure = new CosineDistanceMeasure(); Searcher bruteSearcher = new BruteSearch(distanceMeasure); bruteSearcher.addAll(dataPoints); Pair<List<List<WeightedThing<Vector>>>, Long> reference = getResultsAndRuntime(bruteSearcher, queries); Pair<List<WeightedThing<Vector>>, Long> referenceSearchFirst = getResultsAndRuntimeSearchFirst(bruteSearcher, queries); double bruteSearchAvgTime = reference.getSecond() / (queries.numRows() * 1.0); System.out.printf("BruteSearch: avg_time(1 query) %f[s]\n", bruteSearchAvgTime); return Arrays.asList(new Object[][]{ // NUM_PROJECTIONS = 3 // SEARCH_SIZE = 10 {new ProjectionSearch(distanceMeasure, 3, 10), dataPoints, queries, reference, referenceSearchFirst}, {new FastProjectionSearch(distanceMeasure, 3, 10), dataPoints, queries, reference, referenceSearchFirst}, // NUM_PROJECTIONS = 5 // SEARCH_SIZE = 5 {new ProjectionSearch(distanceMeasure, 5, 5), dataPoints, queries, reference, referenceSearchFirst}, {new FastProjectionSearch(distanceMeasure, 5, 5), dataPoints, queries, reference, referenceSearchFirst}, } ); }
@Test public void crossValidatedAuc() throws IOException { RandomUtils.useTestSeed(); Random gen = RandomUtils.getRandom(); Matrix data = readCsv("cancer.csv"); CrossFoldLearner lr = new CrossFoldLearner(5, 2, 10, new L1()) .stepOffset(10) .decayExponent(0.7) .lambda(1 * 1.0e-3) .learningRate(5); int k = 0; int[] ordering = permute(gen, data.numRows()); for (int epoch = 0; epoch < 100; epoch++) { for (int row : ordering) { lr.train(row, (int) data.get(row, 9), data.viewRow(row)); System.out.printf("%d,%d,%.3f\n", epoch, k++, lr.auc()); } assertEquals(1, lr.auc(), 0.2); } assertEquals(1, lr.auc(), 0.1); }