def test_random(self):
"""
Test random sampling of mixture node
"""
o = 1e-20
X = Mixture([1, 0, 2], Categorical,
[[o, o, o, 1], [o, o, 1, o], [1, o, o, o]])
x = X.random()
self.assertAllClose(x, [2, 3, 0])
pass
def test_random(self):
"""
Test random sampling of mixture node
"""
o = 1e-20
X = Mixture([1, 0, 2], Categorical, [ [o, o, o, 1],
[o, o, 1, o],
[1, o, o, o] ])
x = X.random()
self.assertAllClose(x, [2, 3, 0])
pass
Lambda = Wishart(D, 1e-5 * np.identity(D), plates=(K, ), name='Lambda')
Y = Mixture(Z, Gaussian, mu, Lambda, name='Y')
Z.initialize_from_random()
Q = VB(Y, mu, Lambda, Z, alpha)
Y.observe(y)
Q.update(repeat=1000)
bpplt.gaussian_mixture_2d(Y, alpha=alpha, scale=2)
Q.compute_lowerbound()
Y.random()
from sklearn.mixture import BayesianGaussianMixture
# DD
fin_gmm = BayesianGaussianMixture(
weight_concentration_prior_type="dirichlet_distribution",
covariance_type='full',
weight_concentration_prior=1.2,
n_components=10,
reg_covar=0,
init_params='random',
max_iter=1500,
mean_precision_prior=.8)
fitted = fin_gmm.fit(X)