def test_log_post_pred_k():
# Setup densities
prior = NIW(m_0=np.array([0.0, 0.0]), k_0=2.0, v_0=5.0, S_0=5.0 * np.eye(2))
gmm = GaussianComponents(np.array([[1.2, 0.9], [-0.1, 0.8], [0.5, 0.4]]), prior)
# Add data vectors to a single component
gmm.add_item(0, 0)
gmm.add_item(1, 0)
# Calculate log predictave
lp = gmm.log_post_pred_k(2, 0)
lp_expected = -2.07325364088
npt.assert_almost_equal(lp, lp_expected)
def test_log_post_pred_k():
# Setup densities
prior = NIW(m_0=np.array([0.0, 0.0]), k_0=2., v_0=5., S_0=5. * np.eye(2))
gmm = GaussianComponents(np.array([[1.2, 0.9], [-0.1, 0.8], [0.5, 0.4]]),
prior)
# Add data vectors to a single component
gmm.add_item(0, 0)
gmm.add_item(1, 0)
# Calculate log predictave
lp = gmm.log_post_pred_k(2, 0)
lp_expected = -2.07325364088
npt.assert_almost_equal(lp, lp_expected)
def test_map():
# Setup densities
prior = NIW(m_0=np.array([0.0, 0.0]), k_0=2.0, v_0=5.0, S_0=5.0 * np.eye(2))
gmm = GaussianComponents(np.array([[1.2, 0.9], [-0.1, 0.8]]), prior)
gmm.add_item(0, 0)
gmm.add_item(1, 0)
mu_expected = np.array([0.275, 0.425])
sigma_expected = np.array([[0.55886364, 0.04840909], [0.04840909, 0.52068182]])
# Calculate the posterior MAP of the parameters
mu, sigma = gmm.map(0)
npt.assert_almost_equal(mu, mu_expected)
npt.assert_almost_equal(sigma, sigma_expected)
def test_map():
# Setup densities
prior = NIW(m_0=np.array([0.0, 0.0]),
k_0=2.0,
v_0=5.0,
S_0=5.0 * np.eye(2))
gmm = GaussianComponents(np.array([[1.2, 0.9], [-0.1, 0.8]]), prior)
gmm.add_item(0, 0)
gmm.add_item(1, 0)
mu_expected = np.array([0.275, 0.425])
sigma_expected = np.array([[0.55886364, 0.04840909],
[0.04840909, 0.52068182]])
# Calculate the posterior MAP of the parameters
mu, sigma = gmm.map(0)
npt.assert_almost_equal(mu, mu_expected)
npt.assert_almost_equal(sigma, sigma_expected)
