// ============================================================================
// Copyright 2006-2012 Daniel W. Dyer
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// ============================================================================
package org.uncommons.maths.random;
import java.util.Random;
import org.testng.annotations.Test;
import org.uncommons.maths.Maths;
import org.uncommons.maths.number.AdjustableNumberGenerator;
import org.uncommons.maths.number.NumberGenerator;
import org.uncommons.maths.statistics.DataSet;
/**
* Unit test for the Binomial number generator.
* @author Daniel Dyer
*/
public class BinomialGeneratorTest
{
private final Random rng = new MersenneTwisterRNG();
/**
* Check that the observed mean and standard deviation are consistent
* with the specified distribution parameters.
*/
@Test(groups = "non-deterministic")
public void testDistribution()
{
final int n = 20;
final double p = 0.163d;
NumberGenerator<Integer> generator = new BinomialGenerator(n, // Number of trials.
p, // Probability of success in each.
rng);
checkDistribution(generator, n, p);
}
@Test(groups = "non-deterministic")
public void testDynamicParameters()
{
final int initialN = 20;
final double initialP = 0.163d;
AdjustableNumberGenerator<Integer> nGenerator = new AdjustableNumberGenerator<Integer>(initialN);
AdjustableNumberGenerator<Double> pGenerator = new AdjustableNumberGenerator<Double>(initialP);
NumberGenerator<Integer> generator = new BinomialGenerator(nGenerator,
pGenerator,
rng);
checkDistribution(generator, initialN, initialP);
// Adjust parameters and ensure that the generator output conforms to this new distribution.
final int adjustedN = 14;
nGenerator.setValue(adjustedN);
final double adjustedP = 0.32d;
pGenerator.setValue(adjustedP);
checkDistribution(generator, adjustedN, adjustedP);
}
/**
* The probability of succes in any single trial is invalid if it is greater than 1.
* Further, it needs to be less than 1 to be useful. This test ensures that an
* appropriate exception is thrown if the probability is greater than or equal to 1.
* Not throwing an exception is an error because it permits undetected bugs in
* programs that use {@link BinomialGenerator}.
*/
@Test(expectedExceptions = IllegalArgumentException.class)
public void testProbabilityTooHigh()
{
new BinomialGenerator(5, 1d, rng);
}
/**
* The probability of succes in any single trial must be greater than zero to
* be useful. This test ensures that an appropriate exception is thrown if the
* probability is not positive. Not throwing an exception is an error because it
* permits undetected bugs in programs that use {@link BinomialGenerator}.
*/
@Test(expectedExceptions = IllegalArgumentException.class)
public void testProbabilityTooLow()
{
new BinomialGenerator(5, 0d, rng);
}
/**
* The number of trials must be greater than zero to be useful. This test ensures
* that an appropriate exception is thrown if the number is not positive. Not
* throwing an exception is an error because it permits undetected bugs in
* programs that use {@link BinomialGenerator}.
*/
@Test(expectedExceptions = IllegalArgumentException.class)
public void testTrialsTooLow()
{
new BinomialGenerator(0, 0.5d, rng);
}
private void checkDistribution(NumberGenerator<Integer> generator,
int n,
double p)
{
final double expectedMean = n * p;
final double expectedStandardDeviation = Math.sqrt(n * p * (1 - p));
final int iterations = 10000;
DataSet data = new DataSet(iterations);
for (int i = 0; i < iterations; i++)
{
int value = generator.nextValue();
assert value >= 0 && value <= n : "Value out-of-range: " + value;
data.addValue(value);
}
assert Maths.approxEquals(data.getArithmeticMean(), expectedMean, 0.02d)
: "Observed mean outside acceptable range: " + data.getArithmeticMean();
assert Maths.approxEquals(data.getSampleStandardDeviation(), expectedStandardDeviation, 0.02)
: "Observed standard deviation outside acceptable range: " + data.getSampleStandardDeviation();
}
}