def test_sample_mixed(self):
# test that sampling works also when we have both covs = 0 and
# covs != 0
assets_num = 1
samples_num = 1
correlation = 0.37
epsilons = scientific.make_epsilons(
numpy.zeros((assets_num, samples_num)),
seed=17, correlation=correlation)
self.dist = scientific.LogNormalDistribution(epsilons)
samples = self.dist.sample(numpy.array([0., 0., .1, .1]),
numpy.array([0., .1, 0., .1]),
None, slice(None)).reshape(-1)
numpy.testing.assert_allclose([0., 0., 0.1, 0.10228396], samples)
def test_init(self):
assets_num = 100
samples_num = 1000
correlation = 0.37
epsilons = scientific.make_epsilons(
numpy.zeros((assets_num, samples_num)),
seed=17, correlation=correlation)
self.dist = scientific.LogNormalDistribution(epsilons)
tol = 0.1
for a1, a2 in scientific.pairwise(range(assets_num)):
coeffs = numpy.corrcoef(
self.dist.epsilons[a1, :], self.dist.epsilons[a2, :])
numpy.testing.assert_allclose([1, 1], [coeffs[0, 0], coeffs[1, 1]])
numpy.testing.assert_allclose(
correlation, coeffs[0, 1], rtol=0, atol=tol)
numpy.testing.assert_allclose(
correlation, coeffs[1, 0], rtol=0, atol=tol)