@Test public void testSetState() { Exponential dist = new Exponential(13.0, null); for (double lambda = 0.1; lambda < 1000; lambda *= 1.3) { dist.setState(lambda); checkPdf(dist, lambda); } }
private static void checkEmpiricalDistribution(Exponential dist, int n, double lambda) { double[] x = new double[n]; for (int i = 0; i < n; i++) { x[i] = dist.nextDouble(); } Arrays.sort(x); for (int i = 0; i < n; i++) { double cumulative = (double) i / (n - 1); assertEquals(String.format("lambda = %.3f", lambda), cumulative, dist.cdf(x[i]), 0.02); } }
private static void checkPdf(Exponential dist, double lambda) { assertEquals(0, dist.pdf(-1), 0); double sum = 0; double dx = 0.001 / lambda; for (double x = 0; x < 20/lambda; x+=dx) { sum += x * dist.pdf(x) * dx; assertEquals(Math.exp(-x * lambda) * lambda, dist.pdf(x), 1.0e-9); } assertEquals(1 / lambda, sum, 1.0e-6 / lambda); }
@Test public void testToString() { assertEquals("org.apache.mahout.math.jet.random.Exponential(3.1000)", new Exponential(3.1, null).toString()); assertEquals("org.apache.mahout.math.jet.random.Exponential(3.1000)", new Exponential(3.1, null).toString()); } }
Exponential interval = new Exponential(1 / averageInterval, gen); while (t < end) { ObjectNode transaction = new ObjectNode(nodeFactory); t += interval.nextDouble(); date.setTime((long) t); transaction.set("timestamp", new LongNode((long) (t / 1000)));
@Test public void testCdf() { Exponential dist = new Exponential(5.0, RandomUtils.getRandom()); for (int i = 0; i < 1000; i++) { double x = i / 50.0; assertEquals(1 - Math.exp(-x * 5.0), dist.cdf(x), 1.0e-9); } }
@Test public void testPdf() { checkPdf(new Exponential(13.0, null), 13.0); }
/** * Provides a negative exponential distribution given a rate parameter lambda and an underlying * random number generator. The mean of this distribution will be equal to 1/lambda. * * @param lambda The rate parameter of the distribution. * @param randomGenerator The PRNG that is used to generate values. */ public Exponential(double lambda, Random randomGenerator) { setRandomGenerator(randomGenerator); this.lambda = lambda; }
/** * Returns a random number from the distribution. */ @Override public double nextDouble() { return -Math.log1p(-randomDouble()) / lambda; }
@Test public void copyLearnsAsExpected() { Random gen = RandomUtils.getRandom(); Exponential exp = new Exponential(0.5, gen); Vector beta = new DenseVector(200); for (Vector.Element element : beta.all()) { sign = -1; element.set(sign * exp.nextDouble());
@Test public void testCdf() { Exponential dist = new Exponential(5.0, RandomUtils.getRandom()); for (int i = 0; i < 1000; i++) { double x = i / 50.0; assertEquals(1 - Math.exp(-x * 5.0), dist.cdf(x), 1.0e-9); } }
@Test public void testToString() { assertEquals("org.apache.mahout.math.jet.random.Exponential(3.1000)", new Exponential(3.1, null).toString()); assertEquals("org.apache.mahout.math.jet.random.Exponential(3.1000)", new Exponential(3.1, null).toString()); } }
public User(InetAddress address, String geoCode, TermGenerator terms, double period) { this.terms = terms; this.geoCode = geoCode; this.address = address; this.rate = period; this.sessionTimeDistribution = new Exponential(period, RandomUtils.getRandom()); id = idCounter.addAndGet(1); nextSession = sessionTimeDistribution.nextDouble(); }
/** * Provides a negative exponential distribution given a rate parameter lambda and an underlying * random number generator. The mean of this distribution will be equal to 1/lambda. * * @param lambda The rate parameter of the distribution. * @param randomGenerator The PRNG that is used to generate values. */ public Exponential(double lambda, Random randomGenerator) { setRandomGenerator(randomGenerator); this.lambda = lambda; }
/** * Returns a random number from the distribution. */ @Override public double nextDouble() { return -Math.log1p(-randomDouble()) / lambda; }
@Test public void consistency() { Exponential dist = new Exponential(1, RandomUtils.getRandom()); // deciles computed using R double[] breaks = {0.1053605, 0.2231436, 0.3566749, 0.5108256, 0.6931472, 0.9162907, 1.2039728, 1.6094379, 2.3025851}; for (double lambda : new double[]{0.01, 0.1, 1, 2, 5, 100}) { dist.setState(lambda); DistributionChecks.checkDistribution(dist, breaks, 0, 1 / lambda, 10000); } } @Test
Exponential exp = new Exponential(0.5, gen); Vector beta = new DenseVector(200); for (Vector.Element element : beta.all()) { sign = -1; element.set(sign * exp.nextDouble());
private static void checkEmpiricalDistribution(Exponential dist, int n, double lambda) { double[] x = new double[n]; for (int i = 0; i < n; i++) { x[i] = dist.nextDouble(); } Arrays.sort(x); for (int i = 0; i < n; i++) { double cumulative = (double) i / (n - 1); assertEquals(String.format("lambda = %.3f", lambda), cumulative, dist.cdf(x[i]), 0.02); } }
@Test public void testPdf() { checkPdf(new Exponential(13.0, null), 13.0); }
/** * Provides a negative exponential distribution given a rate parameter lambda and an underlying * random number generator. The mean of this distribution will be equal to 1/lambda. * * @param lambda The rate parameter of the distribution. * @param randomGenerator The PRNG that is used to generate values. */ public Exponential(double lambda, Random randomGenerator) { setRandomGenerator(randomGenerator); this.lambda = lambda; }