List of usage examples for java.util Random nextDouble
public double nextDouble()
From source file:org.ensor.fftmusings.rnn2.GravesLSTMCharModellingExample.java
/** * Given a probability distribution over discrete classes, sample from the * distribution and return the generated class index. * * @param distribution Probability distribution over classes. Must sum to * 1.0//from w w w .j a v a 2 s .co m */ public static int sampleFromDistribution(double[] distribution, Random rng) { double d = 0.0; double sum = 0.0; for (int t = 0; t < 10; t++) { d = rng.nextDouble(); sum = 0.0; for (int i = 0; i < distribution.length; i++) { sum += distribution[i]; if (d <= sum) { return i; } } //If we haven't found the right index yet, maybe the sum is slightly //lower than 1 due to rounding error, so try again. } //Should be extremely unlikely to happen if distribution is a valid probability distribution throw new IllegalArgumentException("Distribution is invalid? d=" + d + ", sum=" + sum); }
From source file:com.insightml.evaluation.simulation.SplitSimulation.java
public static <S extends Sample> Pair<Iterable<S>, List<S>> split(final Iterable<S> instances, final double trainFraction, final Random random) { if (trainFraction == 1.0) { return new Pair<>(instances, null); }//from w ww.j a v a 2s . co m final List<S> train = new LinkedList<>(); final List<S> test = new LinkedList<>(); for (final S sample : instances) { if (random.nextDouble() < trainFraction) { train.add(sample); } else { test.add(sample); } } return new Pair<>(train, test); }
From source file:eu.stratosphere.nephele.services.iomanager.IOManagerITCase.java
private static final int skewedSample(Random rnd, int max) { double uniform = rnd.nextDouble(); double var = Math.pow(uniform, 8.0); double pareto = 0.2 / var; int val = (int) pareto; return val > max ? val % max : val; }
From source file:org.janusgraph.TestBed.java
private static final void doSomethingExpensive(int milliseconds) { double d = 0.0; Random r = new Random(); for (int i = 0; i < 10000 * milliseconds; i++) d += Math.pow(1.1, r.nextDouble()); }
From source file:com.phoenixst.plexus.examples.RandomGraphFactory.java
/** * Creates a random graph according to the Watts-Strogatz model. * The number <code>d</code> here is half of <code>K</code> in * the standard literature.// ww w . ja v a2 s.c om * * <P>Start with a circulant graph. Arrange the nodes in a * circle, starting at 0 and increasing, in order, clockwise. * Begin with node 0 and the edge which connects it to its * nearest clockwise neighbor, which is node 1. With probability * <code>prob</code>, reconnect this edge from node 0 to a * uniformly randomly selected node, with duplicate and self edges * forbidden. Repeat this process for each node, moving * clockwise around the circle. Now, repeat the entire cycle, * but instead choose edges which connect nodes to their * second-nearest clockwise neighbor. And so on, until every one * of the original edges has been considered. */ public static Graph createWattsStrogatz(int n, int d, double prob) { if (prob < 0.0 || prob > 1.0) { throw new IllegalArgumentException("Probability must be between 0.0 and 1.0, inclusive."); } Graph graph = new DefaultGraph(new CirculantGraph(n, d)); Random random = new Random(); for (int dist = 1; dist <= d; dist++) { for (int nodeIndex = 0; nodeIndex < n; nodeIndex++) { if (random.nextDouble() < prob) { Object tail = new Integer(nodeIndex); Object head = new Integer((nodeIndex + dist) % n); Predicate edgePred = EdgePredicateFactory.createEqualsNodes(tail, head, GraphUtils.ANY_DIRECTION_MASK); graph.removeEdge(graph.getEdge(edgePred)); while (true) { head = new Integer(random.nextInt(n)); Predicate traverserPred = TraverserPredicateFactory.createEqualsNode(head, GraphUtils.ANY_DIRECTION_MASK); if (!tail.equals(head) && graph.getIncidentEdge(tail, traverserPred) == null) { graph.addEdge(null, tail, head, true); break; } } } } } return graph; }
From source file:edu.iu.daal_als.SGDUtil.java
public static void randomize(Random random, double[] row, int size, double oneOverSqrtR) { // Non-zero initialization for (int i = 0; i < size; i++) { double rowi = 0.0; do {//from w w w . j av a 2 s. co m rowi = random.nextDouble(); } while (rowi == 0.0); row[i] = rowi * oneOverSqrtR; } }
From source file:com.example.geomesa.kafka.KafkaQuickStart.java
public static void addDeleteNewFeature(SimpleFeatureType sft, FeatureStore producerFS) throws InterruptedException, IOException { SimpleFeatureBuilder builder = new SimpleFeatureBuilder(sft); DefaultFeatureCollection featureCollection = new DefaultFeatureCollection(); final Random random = new Random(); String id = "1000"; builder.add("Antoninus"); // name builder.add((int) Math.round(random.nextDouble() * 110)); // age builder.add(new Date()); // dtg builder.add(WKTUtils$.MODULE$.read("POINT(-1 -1)")); // geom SimpleFeature feature = builder.buildFeature(id); featureCollection.add(feature);/*from w w w .jav a 2 s .c o m*/ producerFS.addFeatures(featureCollection); FilterFactory2 ff = CommonFactoryFinder.getFilterFactory2(); Filter idFilter = ff.id(ff.featureId(id)); producerFS.removeFeatures(idFilter); }
From source file:org.ensor.fftmusings.autoencoder.RNNTrainer.java
public static void evaluateModel(MultiLayerNetwork model, LossMixtureDensity cost, MultiLayerNetwork stackedAutoencoder, Random rng, int epoch) { //Create input for initialization INDArray initialInput = Nd4j.zeros(100); for (int i = 0; i < 100; i++) { initialInput.putScalar(i, rng.nextDouble()); }/*from ww w . ja v a2s. c o m*/ model.rnnClearPreviousState(); Iterator<INDArray> rnnSampleIterator = new RNNSampleIterator(model, cost, stackedAutoencoder, initialInput, rng); new Pipeline(new Layer.ToFFTPNG("data/daa/dct-magnitude-" + epoch + ".png", true)) .add(new Layer.INDArrayToFFTD()).add(new FFTOverlap.NormalizeToHearing(false, 11025)) .add(new FFTOverlap.ReversePhaseDelta(1024)).add(new ChannelDuplicator(AudioSample.class, 2)) .add(WAVFileWriter.create("data/daa/sample-" + epoch + ".wav")).execute(rnnSampleIterator); }
From source file:org.deeplearning4j.legacyExamples.rnn.SparkLSTMCharacterExample.java
/** * Given a probability distribution over discrete classes, sample from the distribution * and return the generated class index. * * @param distribution Probability distribution over classes. Must sum to 1.0 *//*from ww w . j a v a 2s.com*/ private static int sampleFromDistribution(double[] distribution, Random rng) { double d = rng.nextDouble(); double sum = 0.0; for (int i = 0; i < distribution.length; i++) { sum += distribution[i]; if (d <= sum) return i; } //Should never happen if distribution is a valid probability distribution throw new IllegalArgumentException("Distribution is invalid? d=" + d + ", sum=" + sum); }
From source file:view.ChooseFile.java
private static double generateCyclicRandomNumber() { Random r = new Random(); double randomValue = rangeMin + (rangeMax - rangeMin) * r.nextDouble(); return randomValue; }