public void testIncrementalStatistics()
{
// Testing incremental statistics
MersenneTwisterUniformRng mt = new MersenneTwisterUniformRng(42);
IncrementalStatistics stat = new IncrementalStatistics();
for (int i = 0; i < 500000; ++i)
{
double x = 2.0 * (mt.nextReal() - 0.5) * 1234.0;
double w = mt.nextReal();
stat.add(x, w);
}
if (stat.samples() != 500000)
{
QAssert.Fail("stat.samples() (" + stat.samples() + ") can not be reproduced against cached result (" + 500000 + ")");
}
TEST_INC_STAT(stat.weightSum(), 2.5003623600676749e+05);
TEST_INC_STAT(stat.mean(), 4.9122325964293845e-01);
TEST_INC_STAT(stat.variance(), 5.0706503959683329e+05);
TEST_INC_STAT(stat.standardDeviation(), 7.1208499464378076e+02);
TEST_INC_STAT(stat.errorEstimate(), 1.0070402569876076e+00);
TEST_INC_STAT(stat.skewness(), -1.7360169326720038e-03);
TEST_INC_STAT(stat.kurtosis(), -1.1990742562085395e+00);
TEST_INC_STAT(stat.min(), -1.2339945045639761e+03);
TEST_INC_STAT(stat.max(), 1.2339958308008499e+03);
TEST_INC_STAT(stat.downsideVariance(), 5.0786776146975247e+05);
TEST_INC_STAT(stat.downsideDeviation(), 7.1264841364431061e+02);
// This is a test for numerical stability, actual implementation fails
//InverseCumulativeRng<MersenneTwisterUniformRng,InverseCumulativeNormal> normal_gen =
// new InverseCumulativeRng<MersenneTwisterUniformRng, InverseCumulativeNormal>(mt);
//IncrementalStatistics stat2 = new IncrementalStatistics();
//for (int i = 0; i < 500000; ++i)
//{
// double x = normal_gen.next().value * 1E-1 + 1E8;
// double w = 1.0;
// stat2.add(x, w);
//}
//double tol = 1E-5;
//if(Math.Abs( stat2.variance() - 1E-2 ) > tol)
// QAssert.Fail("variance (" + stat2.variance() + ") out of expected range " + 1E-2 + " +- " + tol);
}
