def normal_halton5(sampleSize, numberOfDraws):
unif = draws.getHaltonDraws(sampleSize,
numberOfDraws,
base=2,
skip=10)
return draws.getNormalWichuraDraws(sampleSize,
numberOfDraws,
uniformNumbers=unif,
antithetic=False)
def halton13_anti(sampleSize, numberOfDraws):
"""
The user can define new draws. For example, Halton draws with base 13, skipping
the first 10 draws.
"""
d = draws.getHaltonDraws(sampleSize,
int(numberOfDraws / 2),
base=13,
skip=10)
return np.concatenate((d, 1 - d), axis=1)
def test_halton(self):
halton = dr.getHaltonDraws(sampleSize=1, numberOfDraws=100, base=3)
h = np.array([
0.33333333, 0.66666667, 0.11111111, 0.44444444, 0.77777778,
0.22222222, 0.55555556, 0.88888889, 0.03703704, 0.37037037,
0.7037037, 0.14814815, 0.48148148, 0.81481481, 0.25925926,
0.59259259, 0.92592593, 0.07407407, 0.40740741, 0.74074074,
0.18518519, 0.51851852, 0.85185185, 0.2962963, 0.62962963,
0.96296296, 0.01234568, 0.34567901, 0.67901235, 0.12345679,
0.45679012, 0.79012346, 0.2345679, 0.56790123, 0.90123457,
0.04938272, 0.38271605, 0.71604938, 0.16049383, 0.49382716,
0.82716049, 0.27160494, 0.60493827, 0.9382716, 0.08641975,
0.41975309, 0.75308642, 0.19753086, 0.5308642, 0.86419753,
0.30864198, 0.64197531, 0.97530864, 0.02469136, 0.35802469,
0.69135802, 0.13580247, 0.4691358, 0.80246914, 0.24691358,
0.58024691, 0.91358025, 0.0617284, 0.39506173, 0.72839506,
0.17283951, 0.50617284, 0.83950617, 0.28395062, 0.61728395,
0.95061728, 0.09876543, 0.43209877, 0.7654321, 0.20987654,
0.54320988, 0.87654321, 0.32098765, 0.65432099, 0.98765432,
0.00411523, 0.33744856, 0.67078189, 0.11522634, 0.44855967,
0.781893, 0.22633745, 0.55967078, 0.89300412, 0.04115226,
0.3744856, 0.70781893, 0.15226337, 0.48559671, 0.81893004,
0.26337449, 0.59670782, 0.93004115, 0.0781893, 0.41152263
])
diff = halton - h
norm = np.linalg.norm(diff)
self.assertAlmostEqual(norm, 0, 7)
halton = dr.getHaltonDraws(sampleSize=1,
numberOfDraws=10,
base=3,
skip=10)
h = [
0.7037037, 0.14814815, 0.48148148, 0.81481481, 0.25925926,
0.59259259, 0.92592593, 0.07407407, 0.40740741, 0.74074074
]
diff = halton - h
norm = np.linalg.norm(diff)
self.assertAlmostEqual(norm, 0, 7)
def symm_halton5(sampleSize, numberOfDraws):
return draws.getHaltonDraws(sampleSize,
numberOfDraws,
symmetric=True,
base=5,
skip=10)
def halton5(sampleSize, numberOfDraws):
return draws.getHaltonDraws(sampleSize,
numberOfDraws,
base=5,
skip=10)
def halton13(sampleSize, numberOfDraws):
"""
The user can define new draws. For example, Halton draws
with base 13, skipping the first 10 draws.
"""
return draws.getHaltonDraws(sampleSize, numberOfDraws, base=13, skip=10)