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 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)
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)
