/** * @param rdg random number generator to use * @return a hyperparameter value chosen from Normal(around, step) */ @Override public Double getRandomValue(RandomDataGenerator rdg) { return rdg.nextGaussian(around, step); }
/** * Gets a Gaussian distributed random value with mean = mu * and standard deviation = sigma. * * @return random Gaussian value * @throws MathIllegalArgumentException if the underlying random generator thwrows one */ private double getNextGaussian() throws MathIllegalArgumentException { return randomData.nextGaussian(mu, sigma); }
/** {@inheritDoc} */ public double nextGaussian(double mu, double sigma) throws NotStrictlyPositiveException { return delegate.nextGaussian(mu,sigma); }
/** * @param rdg random number generator to use * @return a hyperparameter value chosen from Normal(around, step) and rounded to the nearest integer */ @Override public Integer getRandomValue(RandomDataGenerator rdg) { return (int) Math.round(rdg.nextGaussian(around, step)); }
/** {@inheritDoc} */ public double nextGaussian(double mu, double sigma) throws NotStrictlyPositiveException { return delegate.nextGaussian(mu,sigma); }
/** {@inheritDoc} */ public double nextGaussian(double mu, double sigma) throws NotStrictlyPositiveException { return delegate.nextGaussian(mu,sigma); }
/** * @param rdg random number generator to use * @return a hyperparameter value chosen from Normal(around, step) */ @Override public Double getRandomValue(RandomDataGenerator rdg) { return rdg.nextGaussian(around, step); }
/** * Gets a Gaussian distributed random value with mean = mu * and standard deviation = sigma. * * @return random Gaussian value * @throws MathIllegalArgumentException if the underlying random generator thwrows one */ private double getNextGaussian() throws MathIllegalArgumentException { return randomData.nextGaussian(mu, sigma); }
/** * Creates an array of examinee ability parameters using random draws from a standard normal quadrature. */ private void drawSimulees(){ theta = new double[nPeople]; for(int i=0;i<nPeople;i++){ theta[i] = random.nextGaussian(0, 1); } }
/** * @param rdg random number generator to use * @return a hyperparameter value chosen from Normal(around, step) and rounded to the nearest integer */ @Override public Integer getRandomValue(RandomDataGenerator rdg) { return (int) Math.round(rdg.nextGaussian(around, step)); }
/** * Generate a date that occurs before the specified date, with a random spread * * @param baseDate * the date before which the generated date will occur * @param meanDaysInThePast * average number of days prior to the specified date that the generated date occurs * @return the generated date */ protected final DateTime generateNormalRandomDateBefore(DateTime baseDate, int meanDaysInThePast) { // we don't care so much about precision... int subtract = Math.abs((int) randomGenerator.nextGaussian(meanDaysInThePast, meanDaysInThePast * 2)); DateTime newDate = baseDate.minusDays(subtract); return newDate; }
RandomDataGenerator generator = new RandomDataGenerator(new Well19937c()); // Unit normal double normDev = generator.nextGaussian(0, 1); // mean = 0.5, std dev = 2 double normDev2 = generator.nextGaussian(0.5, 2); // exponential, mean = 1 double expDev = generator.nextExponential(1);
public long generateTime() { TimeUnit tu = SimulationUtils.getTimeUnit(data); long mean = (long)SimulationUtils.asDouble(data.get(SimulationConstants.MEAN)); mean = timeUnit.convert(mean, tu); long sdv = (long)SimulationUtils.asDouble(data.get(SimulationConstants.STANDARD_DEVIATION)); sdv = timeUnit.convert(sdv, tu); if (sdv > 0) { long value = (long) generator.nextGaussian(mean, sdv); if (value <= 0) { value = mean; } return value; } else { return 0; } }
public long generateTime() { TimeUnit tu = SimulationUtils.getTimeUnit(data); long mean = (long)SimulationUtils.asDouble(data.get(SimulationConstants.MEAN)); mean = timeUnit.convert(mean, tu); long sdv = (long)SimulationUtils.asDouble(data.get(SimulationConstants.STANDARD_DEVIATION)); sdv = timeUnit.convert(sdv, tu); if (sdv > 0) { long value = (long) generator.nextGaussian(mean, sdv); if (value <= 0) { value = mean; } return value; } else { return 0; } }
public FloatProcessor generateGaussianNoise(int width, int height, double mean, double variance) { double sigma = sqrt(variance); FloatProcessor img = new FloatProcessor(width, height); for(int x = 0; x < width; x++) for(int y = 0; y < height; y++) img.setf(x, y, (float)rand.nextGaussian(mean, sigma)); return img; }
recordId = generateRandomID("", 3) + generateRandomID("-", 7); int chargeCount = generatePoissonInt(1, true); int courtCaseLength = (int) randomGenerator.nextGaussian(180, 60); int daysSinceArrest = Days.daysBetween(date, baseDate).getDays(); arrestingAgency = new Agency();