def get_data(nobs):
cop_f = FrankCopula(theta=2)
cd_f = CopulaDistribution(cop_f, [stats.norm, stats.norm])
# np.random.seed(98645713)
# at some seeds, parameters atol-differ from true
# TODO: setting seed doesn't work for copula,
# copula creates new randomly initialized random state, see #7650
rng = np.random.RandomState(98645713)
rvs = cd_f.rvs(nobs, random_state=rng)
assert_allclose(rvs.mean(0), [-0.02936002, 0.06658304], atol=1e-7)
return rvs
# setting the `seed` argument.
# `seed` accepts either an initialized NumPy `Generator` or `RandomState`,
# or any argument acceptable
# to `np.random.default_rng`, e.g., an integer or a sequence of integers.
# This example uses an
# integer.
#
# The singleton `RandomState` that is directly exposed in the `np.random`
# distributions is
# not used, and setting `np.random.seed` has no effect on the values
# generated.
marginals = [stats.gamma(2), stats.norm]
joint_dist = CopulaDistribution(copula=IndependenceCopula(),
marginals=marginals)
sample = joint_dist.rvs(512, random_state=20210801)
h = sns.jointplot(x=sample[:, 0], y=sample[:, 1], kind="scatter")
_ = h.set_axis_labels("X1", "X2", fontsize=16)
# Now, above we have expressed the dependency between our variables using
# a copula, we can use this copula to sample a new set of observation with
# the same convenient class.
joint_dist = CopulaDistribution(copula, marginals)
# Use an initialized Generator object
rng = np.random.default_rng([2, 0, 2, 1, 0, 8, 0, 1])
sample = joint_dist.rvs(512, random_state=rng)
h = sns.jointplot(x=sample[:, 0], y=sample[:, 1], kind="scatter")
_ = h.set_axis_labels("X1", "X2", fontsize=16)
# There are two things to note here. *(i)* as in the independent case, the