def estimate_bivariate_mle_jr(): ndim = 2 size = (10000, ndim) data = np.random.normal(size=size) eta, lam = 4, -.9 skst = SkewStudent(eta=eta, lam=lam) data = skst.rvs(size=size) model = SkStJR(ndim=ndim, data=data) out = model.fit_mle() print(out) model.from_theta(out.x) fig, axes = plt.subplots(nrows=size[1], ncols=1) for innov, ax in zip(data.T, axes): sns.kdeplot(innov, ax=ax, label='data') lines = [ax.get_lines()[0].get_xdata() for ax in axes] lines = np.vstack(lines).T marginals = model.marginals(lines) for line, margin, ax in zip(lines.T, marginals.T, axes): ax.plot(line, margin, label='fitted') ax.legend() plt.show()
def test_init(self): """Test __init__.""" skst = SkStJR(ndim=3) self.assertIsInstance(skst.eta, np.ndarray) self.assertIsInstance(skst.lam, np.ndarray) eta, lam = [10, 15], [.5, 1.5] skst = SkStJR(ndim=len(lam), eta=eta, lam=lam) npt.assert_array_equal(skst.eta, np.array(eta)) npt.assert_array_equal(skst.lam, np.array(lam)) eta, lam = [15, 10], [1.5, .5] skst.from_theta(np.concatenate((eta, lam))) npt.assert_array_equal(skst.eta, np.array(eta)) npt.assert_array_equal(skst.lam, np.array(lam)) size = (10, len(eta)) data = np.random.normal(size=size) skst = SkStJR(ndim=len(lam), data=data) npt.assert_array_equal(skst.data, data)