@Test
public void sample() {
for (double p : new double[] {0.1, 0.2, 0.5, 0.9}) {
for (int r = 1; r < 5; r++) {
NegativeBinomial nb = new NegativeBinomial(r, p, new MersenneTwister(1));
int[] counts = new int[N];
for (int i = 0; i < N; i++) {
int k = nb.nextInt();
if (k < counts.length) {
counts[k]++;
}
}
// probably should do a chi^2 or LLR test here especially since we know the PDF
for (int k = 0; k < counts.length; k++) {
Assert.assertEquals(
String.format(
Locale.ENGLISH,
"r=%d,p=%.3f,k=%d,count=%d,pdf=%.3f",
r,
p,
k,
counts[k],
nb.pdf(k)),
N * nb.pdf(k),
counts[k],
Math.max(3, 4 * Math.sqrt(N * nb.pdf(k) * (1 - nb.pdf(k)))));
}
}
}
}
@Test
public void testDistributionFunctions() throws IOException {
InputSupplier<InputStreamReader> input =
Resources.newReaderSupplier(
Resources.getResource("negative-binomial-test-data.csv"), Charsets.UTF_8);
boolean header = true;
for (String line : CharStreams.readLines(input)) {
if (header) {
// skip
header = false;
} else {
Iterable<String> values = onComma.split(line);
int k = Integer.parseInt(Iterables.get(values, 0));
double p = Double.parseDouble(Iterables.get(values, 1));
int r = Integer.parseInt(Iterables.get(values, 2));
double density = Double.parseDouble(Iterables.get(values, 3));
double cume = Double.parseDouble(Iterables.get(values, 4));
NegativeBinomial nb = new NegativeBinomial(r, p, new MersenneTwister());
Assert.assertEquals("cumulative " + k + ',' + p + ',' + r, cume, nb.cdf(k), cume * 1e-05);
Assert.assertEquals(
"density " + k + ',' + p + ',' + r, density, nb.pdf(k), density * 1e-05);
}
}
}
