def test_normal(self):
draws = dr.getNormalWichuraDraws(sampleSize=3, numberOfDraws=1000000)
mean = np.linalg.norm(np.average(draws, axis=1))
self.assertAlmostEqual(mean, 0, 2)
draws = dr.getNormalWichuraDraws(sampleSize=3,
numberOfDraws=1000000,
antithetic=True)
mean = np.linalg.norm(np.average(draws, axis=1))
self.assertAlmostEqual(mean, 0, 5)
def normal_MLHS_anti(sampleSize, numberOfDraws):
unif = draws.getLatinHypercubeDraws(sampleSize,
int(numberOfDraws / 2))
return draws.getNormalWichuraDraws(sampleSize,
numberOfDraws,
uniformNumbers=unif,
antithetic=True)
def normal_MLHS(sampleSize, numberOfDraws):
unif = draws.getLatinHypercubeDraws(sampleSize,
numberOfDraws)
return draws.getNormalWichuraDraws(sampleSize,
numberOfDraws,
uniformNumbers=unif,
antithetic=False)
def normal_halton5(sampleSize, numberOfDraws):
unif = draws.getHaltonDraws(sampleSize,
numberOfDraws,
base=2,
skip=10)
return draws.getNormalWichuraDraws(sampleSize,
numberOfDraws,
uniformNumbers=unif,
antithetic=False)
def normal_antithetic(sampleSize, numberOfDraws):
return draws.getNormalWichuraDraws(sampleSize=sampleSize,
numberOfDraws=numberOfDraws,
antithetic=True)