def test_log_prod_norm():
np.random.seed(1)
# Prior
D = 10
var = 1*np.random.rand(D)
mu_0 = 5*np.random.rand(D) - 2
var_0 = 2*np.random.rand(D)
prior = FixedVarPrior(var, mu_0, var_0)
# GMM will be used to access `_log_prod_norm`
x = 3*np.random.rand(D) + 4
gmm = GaussianComponentsFixedVar(np.array([x]), prior)
expected_prior = np.sum([log_norm_pdf(x[i], mu_0[i], var_0[i]) for i in range(len(x))])
npt.assert_almost_equal(gmm.log_prior(0), expected_prior)
def test_log_prod_norm():
np.random.seed(1)
# Prior
D = 10
var = 1 * np.random.rand(D)
mu_0 = 5 * np.random.rand(D) - 2
var_0 = 2 * np.random.rand(D)
prior = FixedVarPrior(var, mu_0, var_0)
# GMM will be used to access `_log_prod_norm`
x = 3 * np.random.rand(D) + 4
gmm = GaussianComponentsFixedVar(np.array([x]), prior)
expected_prior = np.sum(
[log_norm_pdf(x[i], mu_0[i], var_0[i]) for i in range(len(x))])
npt.assert_almost_equal(gmm.log_prior(0), expected_prior)
