// ============================================================================
// 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 normally-distributed number generator.
* @author Daniel Dyer
*/
public class GaussianGeneratorTest
{
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 double mean = 147;
final double standardDeviation = 17;
NumberGenerator<Double> generator = new GaussianGenerator(mean,
standardDeviation,
rng);
checkDistribution(generator, mean, standardDeviation);
}
@Test(groups = "non-deterministic")
public void testDynamicParameters()
{
final double initialMean = 147;
final double initialStandardDeviation = 17;
AdjustableNumberGenerator<Double> meanGenerator = new AdjustableNumberGenerator<Double>(initialMean);
AdjustableNumberGenerator<Double> standardDeviationGenerator = new AdjustableNumberGenerator<Double>(initialStandardDeviation);
NumberGenerator<Double> generator = new GaussianGenerator(meanGenerator,
standardDeviationGenerator,
rng);
checkDistribution(generator, initialMean, initialStandardDeviation);
// Adjust parameters and ensure that the generator output conforms to this new
// distribution.
final double adjustedMean = 73;
final double adjustedStandardDeviation = 9;
meanGenerator.setValue(adjustedMean);
standardDeviationGenerator.setValue(adjustedStandardDeviation);
checkDistribution(generator, adjustedMean, adjustedStandardDeviation);
}
private void checkDistribution(NumberGenerator<Double> generator,
double expectedMean,
double expectedStandardDeviation)
{
final int iterations = 10000;
DataSet data = new DataSet(iterations);
for (int i = 0; i < iterations; i++)
{
data.addValue(generator.nextValue());
}
assert Maths.approxEquals(data.getArithmeticMean(), expectedMean, 0.02)
: "Observed mean outside acceptable range: " + data.getArithmeticMean();
assert Maths.approxEquals(data.getSampleStandardDeviation(), expectedStandardDeviation, 0.02)
: "Observed standard deviation outside acceptable range: " + data.getSampleStandardDeviation();
// Expected median is the same as expected mean.
assert Maths.approxEquals(data.getMedian(), expectedMean, 0.02)
: "Observed median outside acceptable range: " + data.getMedian();
}
}