public void TestCumulativeDistribution()
{
// Standard normal.
var dist = new BoxMullerGaussianDistribution(0.0, 1.0);
GaussianDistributionTestUtils.TestDistribution(dist, 0.0, 1.0);
// Non-zero mean tests.
dist = new BoxMullerGaussianDistribution(10.0, 1.0);
GaussianDistributionTestUtils.TestDistribution(dist, 10.0, 1.0);
dist = new BoxMullerGaussianDistribution(-100.0, 1.0);
GaussianDistributionTestUtils.TestDistribution(dist, -100.0, 1.0);
// Non-1.0 standard deviations
dist = new BoxMullerGaussianDistribution(0.0, 0.2);
GaussianDistributionTestUtils.TestDistribution(dist, 0.0, 0.2);
dist = new BoxMullerGaussianDistribution(0.0, 5.0);
GaussianDistributionTestUtils.TestDistribution(dist, 0.0, 5.0);
// Non-zero mean and non-1.0 standard deviation.
dist = new BoxMullerGaussianDistribution(10.0, 2.0);
GaussianDistributionTestUtils.TestDistribution(dist, 10.0, 2.0);
dist = new BoxMullerGaussianDistribution(-10.0, 3.0);
GaussianDistributionTestUtils.TestDistribution(dist, -10.0, 3.0);
}
public void TestMean()
{
int sampleCount = 10000000;
var dist = new BoxMullerGaussianDistribution();
double[] sampleArr = new double[sampleCount];
for (int i = 0; i < sampleCount; i++)
{
sampleArr[i] = dist.Sample();
}
double sum = Math.Abs(sampleArr.Sum());
double mean = sum / sampleCount;
Assert.IsTrue(Math.Abs(mean) < 0.001);
}
public void TestStandardDeviation()
{
int sampleCount = 10000000;
var dist = new BoxMullerGaussianDistribution();
double sqrSum = 0.0;
for (int i = 0; i < sampleCount; i++)
{
double x = dist.Sample();
sqrSum += x * x;
}
double var = sqrSum / sampleCount;
double stdDev = Math.Sqrt(var);
Assert.IsTrue(Math.Abs(stdDev - 1.0) < 0.001);
}
public void TestSimpleStats()
{
var dist = new BoxMullerGaussianDistribution();
GaussianDistributionTestUtils.TestSimpleStats(dist);
}
