@Test
public void testNextGamma() {
double[] quartiles;
long[] counts;
// Tests shape > 1, one case in the rejection sampling
quartiles = TestUtils.getDistributionQuartiles(new GammaDistribution(4, 2));
counts = new long[4];
randomData.reSeed(1000);
for (int i = 0; i < 1000; i++) {
double value = randomData.nextGamma(4, 2);
TestUtils.updateCounts(value, counts, quartiles);
}
TestUtils.assertChiSquareAccept(expected, counts, 0.001);
// Tests shape <= 1, another case in the rejection sampling
quartiles = TestUtils.getDistributionQuartiles(new GammaDistribution(0.3, 3));
counts = new long[4];
randomData.reSeed(1000);
for (int i = 0; i < 1000; i++) {
double value = randomData.nextGamma(0.3, 3);
TestUtils.updateCounts(value, counts, quartiles);
}
TestUtils.assertChiSquareAccept(expected, counts, 0.001);
}
@Test
public void testNextZipf() {
ZipfDistributionTest testInstance = new ZipfDistributionTest();
int[] densityPoints = testInstance.makeDensityTestPoints();
double[] densityValues = testInstance.makeDensityTestValues();
int sampleSize = 1000;
int length = TestUtils.eliminateZeroMassPoints(densityPoints, densityValues);
ZipfDistribution distribution = (ZipfDistribution) testInstance.makeDistribution();
double[] expectedCounts = new double[length];
long[] observedCounts = new long[length];
for (int i = 0; i < length; i++) {
expectedCounts[i] = sampleSize * densityValues[i];
}
randomData.reSeed(1000);
for (int i = 0; i < sampleSize; i++) {
int value =
randomData.nextZipf(distribution.getNumberOfElements(), distribution.getExponent());
for (int j = 0; j < length; j++) {
if (value == densityPoints[j]) {
observedCounts[j]++;
}
}
}
TestUtils.assertChiSquareAccept(densityPoints, expectedCounts, observedCounts, .001);
}
/** test reseeding, algorithm/provider games */
@Test
public void testConfig() {
randomData.reSeed(1000);
double v = randomData.nextUniform(0, 1);
randomData.reSeed();
Assert.assertTrue("different seeds", Math.abs(v - randomData.nextUniform(0, 1)) > 10E-12);
randomData.reSeed(1000);
Assert.assertEquals("same seeds", v, randomData.nextUniform(0, 1), 10E-12);
randomData.reSeedSecure(1000);
String hex = randomData.nextSecureHexString(40);
randomData.reSeedSecure();
Assert.assertTrue("different seeds", !hex.equals(randomData.nextSecureHexString(40)));
randomData.reSeedSecure(1000);
Assert.assertTrue("same seeds", !hex.equals(randomData.nextSecureHexString(40)));
/*
* remove this test back soon, since it takes about 4 seconds
*
* try { randomData.setSecureAlgorithm("SHA1PRNG","SUN"); } catch
* (NoSuchProviderException ex) { ; } Assert.assertTrue("different seeds",
* !hex.equals(randomData.nextSecureHexString(40))); try {
* randomData.setSecureAlgorithm("NOSUCHTHING","SUN");
* Assert.fail("expecting NoSuchAlgorithmException"); } catch
* (NoSuchProviderException ex) { ; } catch (NoSuchAlgorithmException
* ex) { ; }
*
* try { randomData.setSecureAlgorithm("SHA1PRNG","NOSUCHPROVIDER");
* Assert.fail("expecting NoSuchProviderException"); } catch
* (NoSuchProviderException ex) { ; }
*/
// test reseeding without first using the generators
RandomDataGenerator rd = new RandomDataGenerator();
rd.reSeed(100);
rd.nextLong(1, 2);
RandomDataGenerator rd2 = new RandomDataGenerator();
rd2.reSeedSecure(2000);
rd2.nextSecureLong(1, 2);
rd = new RandomDataGenerator();
rd.reSeed();
rd.nextLong(1, 2);
rd2 = new RandomDataGenerator();
rd2.reSeedSecure();
rd2.nextSecureLong(1, 2);
}
public static void main(String[] args) {
// int d_total = 10000;
int p_numbers = 3; // Partition Numbers
int p_size = 5;
int z_exponent = 1;
int d = 0;
int d_start = 0;
int d_end = p_size;
double mu = 0.0;
double sigma = 1.0;
double z, c, c_rand, n;
ZipfDistribution
zipf; // Parameters = Number of Elements, Exponent
RandomDataGenerator randData = new RandomDataGenerator();
RandomDataGenerator rand = new RandomDataGenerator();
rand.reSeed(0);
for (int p = 0; p < p_numbers; p++) {
zipf = new ZipfDistribution(p_size, z_exponent);
zipf.reseedRandomGenerator(p);
randData.reSeed(p);
for (d = d_start; d < d_end; d++) {
z = randData.nextZipf(p_size, z_exponent);
z += d_start;
if (z > d_end) z -= 1;
c = zipf.cumulativeProbability((d - d_start));
c_rand = zipf.cumulativeProbability(((int) z - d_start));
// n = rand.nextGaussian(mu, sigma);
n = rand.nextUniform(0.0, 1.0);
System.out.println(p + " " + d + " " + c + " " + (int) z + " " + c_rand + " " + n);
}
d_start = d_end;
d_end = (d + p_size);
}
}
@Test
public void testNextWeibull() {
double[] quartiles = TestUtils.getDistributionQuartiles(new WeibullDistribution(1.2, 2.1));
long[] counts = new long[4];
randomData.reSeed(1000);
for (int i = 0; i < 1000; i++) {
double value = randomData.nextWeibull(1.2, 2.1);
TestUtils.updateCounts(value, counts, quartiles);
}
TestUtils.assertChiSquareAccept(expected, counts, 0.001);
}
/** test failure modes and distribution of nextExponential() */
@Test
public void testNextExponential() {
try {
randomData.nextExponential(-1);
Assert.fail("negative mean -- expecting MathIllegalArgumentException");
} catch (MathIllegalArgumentException ex) {
// ignored
}
try {
randomData.nextExponential(0);
Assert.fail("zero mean -- expecting MathIllegalArgumentException");
} catch (MathIllegalArgumentException ex) {
// ignored
}
double[] quartiles;
long[] counts;
// Mean 1
quartiles = TestUtils.getDistributionQuartiles(new ExponentialDistribution(1));
counts = new long[4];
randomData.reSeed(1000);
for (int i = 0; i < 1000; i++) {
double value = randomData.nextExponential(1);
TestUtils.updateCounts(value, counts, quartiles);
}
TestUtils.assertChiSquareAccept(expected, counts, 0.001);
// Mean 5
quartiles = TestUtils.getDistributionQuartiles(new ExponentialDistribution(5));
counts = new long[4];
randomData.reSeed(1000);
for (int i = 0; i < 1000; i++) {
double value = randomData.nextExponential(5);
TestUtils.updateCounts(value, counts, quartiles);
}
TestUtils.assertChiSquareAccept(expected, counts, 0.001);
}
/** test failure modes and distribution of nextGaussian() */
@Test
public void testNextGaussian() {
try {
randomData.nextGaussian(0, 0);
Assert.fail("zero sigma -- MathIllegalArgumentException expected");
} catch (MathIllegalArgumentException ex) {
// ignored
}
double[] quartiles = TestUtils.getDistributionQuartiles(new NormalDistribution(0, 1));
long[] counts = new long[4];
randomData.reSeed(1000);
for (int i = 0; i < 1000; i++) {
double value = randomData.nextGaussian(0, 1);
TestUtils.updateCounts(value, counts, quartiles);
}
TestUtils.assertChiSquareAccept(expected, counts, 0.001);
}
/**
* Make sure that empirical distribution of random Poisson(4)'s has P(X <= 5) close to actual
* cumulative Poisson probability and that nextPoisson fails when mean is non-positive.
*/
@Test
public void testNextPoisson() {
try {
randomData.nextPoisson(0);
Assert.fail("zero mean -- expecting MathIllegalArgumentException");
} catch (MathIllegalArgumentException ex) {
// ignored
}
try {
randomData.nextPoisson(-1);
Assert.fail("negative mean supplied -- MathIllegalArgumentException expected");
} catch (MathIllegalArgumentException ex) {
// ignored
}
try {
randomData.nextPoisson(0);
Assert.fail("0 mean supplied -- MathIllegalArgumentException expected");
} catch (MathIllegalArgumentException ex) {
// ignored
}
final double mean = 4.0d;
final int len = 5;
PoissonDistribution poissonDistribution = new PoissonDistribution(mean);
Frequency f = new Frequency();
randomData.reSeed(1000);
for (int i = 0; i < largeSampleSize; i++) {
f.addValue(randomData.nextPoisson(mean));
}
final long[] observed = new long[len];
for (int i = 0; i < len; i++) {
observed[i] = f.getCount(i + 1);
}
final double[] expected = new double[len];
for (int i = 0; i < len; i++) {
expected[i] = poissonDistribution.probability(i + 1) * largeSampleSize;
}
TestUtils.assertChiSquareAccept(expected, observed, 0.0001);
}
