def test_likelihood(self): """Test log-likelihood.""" eta, lam = 100, [.5, 1.5, 2] theta = np.concatenate((np.atleast_1d(eta), lam)) size = (10, len(lam)) data = np.random.normal(size=size) skst = SkStAC(ndim=len(lam), eta=eta, lam=lam, data=data) logl1 = skst.likelihood(theta) logl2 = skst.likelihood(theta * 2) self.assertIsInstance(logl1, float) self.assertNotEqual(logl1, logl2) npt.assert_array_equal(skst.data, data)
def estimate_bivariate_mle_ac(): size = 2000 eta, lam = 10, [-2, 2] skst = SkStAC(ndim=len(lam), eta=eta, lam=lam) data = skst.rvs(size=size) skst.data = data print(skst.likelihood(np.concatenate(([4000], lam)))) print(skst.likelihood(np.concatenate(([eta], lam)))) # sns.kdeplot(data, shade=True) # plt.axis('square') # plt.xlim([-2, 2]) # plt.ylim([-2, 2]) # plt.show() model = SkStAC(ndim=len(lam), data=data) out = model.fit_mle(method='L-BFGS-B') print(out)