List of usage examples for org.apache.commons.math3.distribution ExponentialDistribution sample
@Override public double sample()
From source file:com.cloudera.oryx.app.traffic.TrafficUtil.java
public static void main(String[] args) throws Exception { if (args.length < 3) { System.err.println("usage: TrafficUtil [hosts] [requestIntervalMS] [threads] [... other args]"); return;/*ww w. ja va2s . c o m*/ } String[] hostStrings = COMMA.split(args[0]); Preconditions.checkArgument(hostStrings.length >= 1); int requestIntervalMS = Integer.parseInt(args[1]); Preconditions.checkArgument(requestIntervalMS >= 0); int numThreads = Integer.parseInt(args[2]); Preconditions.checkArgument(numThreads >= 1); String[] otherArgs = new String[args.length - 3]; System.arraycopy(args, 3, otherArgs, 0, otherArgs.length); List<URI> hosts = Arrays.stream(hostStrings).map(URI::create).collect(Collectors.toList()); int perClientRequestIntervalMS = numThreads * requestIntervalMS; Endpoints alsEndpoints = new Endpoints(ALSEndpoint.buildALSEndpoints()); AtomicLong requestCount = new AtomicLong(); AtomicLong serverErrorCount = new AtomicLong(); AtomicLong clientErrorCount = new AtomicLong(); AtomicLong exceptionCount = new AtomicLong(); long start = System.currentTimeMillis(); ExecUtils.doInParallel(numThreads, numThreads, true, i -> { RandomGenerator random = RandomManager.getRandom(Integer.toString(i).hashCode() ^ System.nanoTime()); ExponentialDistribution msBetweenRequests; if (perClientRequestIntervalMS > 0) { msBetweenRequests = new ExponentialDistribution(random, perClientRequestIntervalMS); } else { msBetweenRequests = null; } ClientConfig clientConfig = new ClientConfig(); PoolingHttpClientConnectionManager connectionManager = new PoolingHttpClientConnectionManager(); connectionManager.setMaxTotal(numThreads); connectionManager.setDefaultMaxPerRoute(numThreads); clientConfig.property(ApacheClientProperties.CONNECTION_MANAGER, connectionManager); clientConfig.connectorProvider(new ApacheConnectorProvider()); Client client = ClientBuilder.newClient(clientConfig); try { while (true) { try { WebTarget target = client.target("http://" + hosts.get(random.nextInt(hosts.size()))); Endpoint endpoint = alsEndpoints.chooseEndpoint(random); Invocation invocation = endpoint.makeInvocation(target, otherArgs, random); long startTime = System.currentTimeMillis(); Response response = invocation.invoke(); try { response.readEntity(String.class); } finally { response.close(); } long elapsedMS = System.currentTimeMillis() - startTime; int statusCode = response.getStatusInfo().getStatusCode(); if (statusCode >= 400) { if (statusCode >= 500) { serverErrorCount.incrementAndGet(); } else { clientErrorCount.incrementAndGet(); } } endpoint.recordTiming(elapsedMS); if (requestCount.incrementAndGet() % 10000 == 0) { long elapsed = System.currentTimeMillis() - start; log.info("{}ms:\t{} requests\t({} client errors\t{} server errors\t{} exceptions)", elapsed, requestCount.get(), clientErrorCount.get(), serverErrorCount.get(), exceptionCount.get()); for (Endpoint e : alsEndpoints.getEndpoints()) { log.info("{}", e); } } if (msBetweenRequests != null) { int desiredElapsedMS = (int) Math.round(msBetweenRequests.sample()); if (elapsedMS < desiredElapsedMS) { Thread.sleep(desiredElapsedMS - elapsedMS); } } } catch (Exception e) { exceptionCount.incrementAndGet(); log.warn("{}", e.getMessage()); } } } finally { client.close(); } }); }
From source file:controller.DistributionController.java
public static void exponential(double media, double extra, int size) { numerosGerados.clear();/*from w w w . ja va2s. co m*/ ExponentialDistribution ex = new ExponentialDistribution(media); for (int i = 0; i < size; i++) { numerosGerados.add(extra + ex.sample()); } }
From source file:com.github.rinde.rinsim.scenario.measure.MetricsTest.java
static Times generateTimes(RandomGenerator rng, double intensity) { final ExponentialDistribution ed = new ExponentialDistribution(1000d / intensity); ed.reseedRandomGenerator(rng.nextLong()); final List<Long> times = newArrayList(); long sum = 0; while (sum < 1000) { sum += DoubleMath.roundToLong(ed.sample(), RoundingMode.HALF_DOWN); if (sum < 1000) { times.add(sum);//from w w w .j av a 2 s. c o m } } return asTimes(1000, times); }
From source file:nl.rivm.cib.episim.model.disease.infection.MSEIRSTest.java
public static Observable<Entry<Double, long[]>> stochasticSellke(final SIRConfig config, final double maxDt) { return Observable.create(sub -> { final double beta = config.reproduction() / config.recovery(); final long[] y = config.population(); final double[] T = config.t(); final double dt = Double.isFinite(maxDt) && maxDt > 0 ? maxDt : T[1]; final Long seed = config.seed(); final RandomGenerator rng = new MersenneTwister(seed == null ? System.currentTimeMillis() : seed); final ExponentialDistribution resistanceDist = new ExponentialDistribution(rng, 1), recoverDist = new ExponentialDistribution(rng, config.recovery()); // pending infections (mapping resistance -> amount) final TreeMap<Double, Integer> tInfect = IntStream.range(0, (int) y[0]) .mapToObj(i -> resistanceDist.sample()) .collect(Collectors.toMap(r -> r, r -> 1, Integer::sum, TreeMap::new)); // pending recoveries (mapping time -> amount) final TreeMap<Double, Integer> tRecover = new TreeMap<>(); double cumPres = 0; // Re-initialize infectives as susceptibles with zero resistance tInfect.put(cumPres, (int) y[1]); y[0] += y[1]; // I -> S y[1] -= y[1]; // I -> 0 for (double t = T[0]; t < T[1];) { publishCopy(sub, t, y);//from w w w . j a v a 2 s .c om final long localPopSize = y[0] + y[1] + y[2]; final Double ri = tInfect.isEmpty() ? null : tInfect.firstKey(), ti = ri == null ? null : // now + remaining resistance per relative pressure t + (ri - cumPres) / (beta * Math.max(y[1], 1) / localPopSize), tr = tRecover.isEmpty() ? null : tRecover.firstKey(); // time of next infection is earliest if (ti != null && (tr == null || ti < tr)) { final int ni = tInfect.remove(ri); cumPres = ri; // publish intermediate values for (double t1 = Math.min(ti, t + dt), tMax = Math.min(T[1], ti); t1 < tMax; t1 += dt) publishCopy(sub, t1, y); // infect t = ti; y[0] -= ni; // from S y[1] += ni; // to I // schedule S_t recoveries at t+Exp(1/gamma) for (int i = 0; i < ni; i++) tRecover.compute(t + recoverDist.sample(), (k, v) -> v == null ? 1 : v + 1); } // time of next recovery is earliest else if (tr != null) { final int nr = tRecover.remove(tr); if (ri != null) // advance cumulative pressure by dt * relative pressure cumPres += (tr - t) * beta * y[1] / localPopSize; // publish intermediate values for (double t1 = Math.min(tr, t + dt), tMax = Math.min(T[1], tr); t1 < tMax; t1 += dt) publishCopy(sub, t1, y); // recover t = tr; y[1] -= nr; // from I y[2] += nr; // to R } // no events remaining else { // publish intermediate values for (double t1 = t + dt; t1 < T[1]; t1 += dt) publishCopy(sub, t1, y); // time ends break; } } sub.onComplete(); }); }
From source file:org.openmhealth.data.generator.service.TimestampedValueGroupGenerationServiceImpl.java
@Override public Iterable<TimestampedValueGroup> generateValueGroups(MeasureGenerationRequest request) { ExponentialDistribution interPointDurationDistribution = new ExponentialDistribution( request.getMeanInterPointDuration().getSeconds()); long totalDurationInS = Duration.between(request.getStartDateTime(), request.getEndDateTime()).getSeconds(); OffsetDateTime effectiveDateTime = request.getStartDateTime(); List<TimestampedValueGroup> timestampedValueGroups = new ArrayList<>(); do {//from w w w . j ava 2 s.c o m effectiveDateTime = effectiveDateTime.plus((long) interPointDurationDistribution.sample(), SECONDS); if (!effectiveDateTime.isBefore(request.getEndDateTime())) { break; } if (request.isSuppressNightTimeMeasures() != null && request.isSuppressNightTimeMeasures() && (effectiveDateTime.getHour() >= NIGHT_TIME_START_HOUR || effectiveDateTime.getHour() < NIGHT_TIME_END_HOUR)) { continue; } TimestampedValueGroup valueGroup = new TimestampedValueGroup(); valueGroup.setTimestamp(effectiveDateTime); double trendProgressFraction = (double) Duration.between(request.getStartDateTime(), effectiveDateTime) .getSeconds() / totalDurationInS; for (Map.Entry<String, BoundedRandomVariableTrend> trendEntry : request.getTrends().entrySet()) { String key = trendEntry.getKey(); BoundedRandomVariableTrend trend = trendEntry.getValue(); double value = trend.nextValue(trendProgressFraction); valueGroup.setValue(key, value); } timestampedValueGroups.add(valueGroup); } while (true); return timestampedValueGroups; }
From source file:SimpleGui.QueryGenerator.java
/** * Returns array of values in a given distribution * @param distribution//from ww w . j ava 2 s. com * @param numQs * @param mean * @param variance * @param exponentialMean * @param u_lower * @param u_upper * @return */ public int[] getDistributionValues(String distribution, int numQs, double mean, double variance, double exponentialMean, int u_lower, int u_upper, double slope, int upperBoundary) { int[] values = new int[numQs]; switch (distribution) { case "Poisson": { RandomEngine engine = new DRand(); Poisson poisson = new Poisson(mean, engine); int poissonObs = poisson.nextInt(); Normal normal = new Normal(mean, variance, engine); for (int i = 0; i < numQs; i++) { double normalObs = normal.nextDouble(); int sample = (int) Math.abs(normalObs); while (sample >= upperBoundary) { normalObs = normal.nextDouble(); sample = (int) Math.abs(normalObs); } values[i] = sample; //System.out.println(values[i]+"from poisson"); //============================================= PoissonDistribution p = new PoissonDistribution(mean); int randomInt = p.sample(); // values[i] = randomInt; } // Arrays.sort(values); break; } case "Random": { Random randomGenerator = new Random(); for (int i = 0; i < numQs; i++) { int randomInt = randomGenerator.nextInt(numQs - 1); values[i] = randomInt + 1; // to avoid '0' } break; } case "Uniform": { for (int i = 0; i < numQs; i++) { UniformIntegerDistribution u = new UniformIntegerDistribution(u_lower, u_upper); int randomInt = u.sample(); while (randomInt >= upperBoundary) { randomInt = u.sample(); } if (randomInt == 0) values[i] = randomInt + 1; // to avoid random '0' else values[i] = randomInt; } break; } case "Exponential": { for (int i = 0; i < numQs; i++) { ExponentialDistribution e = new ExponentialDistribution(exponentialMean); double exponential = e.sample(); while (exponential >= upperBoundary - 1) { exponential = e.sample(); } values[i] = new Double(exponential).intValue() + 1; //to avoid random value '0' } break; } case "Grading": { // y=mx+c. increment and then decrement for positive slopes. int constant = 0; for (int i = 0; i < numQs; i++) { constant += slope; int nextVal = new Double(constant).intValue(); System.out.println(upperBoundary); if (nextVal >= upperBoundary || nextVal < 0) { slope = -1 * slope; i--; } else values[i] = nextVal; } break; } } // Arrays.sort(values); return values; }