from mimo.distributions import Categorical
from mimo.distributions import GaussianWithCovariance
from mimo.distributions import GaussianWithPrecision
from mimo.mixtures import MixtureOfGaussians
npr.seed(1337)
gating = Categorical(K=2)
components = [GaussianWithCovariance(mu=np.array([1., 1.]), sigma=0.25 * np.eye(2)),
GaussianWithCovariance(mu=np.array([-1., -1.]), sigma=0.5 * np.eye(2))]
gmm = MixtureOfGaussians(gating=gating, components=components)
obs, z = gmm.rvs(500)
gmm.plot(obs)
gating = Categorical(K=2)
components = [GaussianWithPrecision(mu=npr.randn(2, ),
lmbda=np.eye(2)) for _ in range(2)]
model = MixtureOfGaussians(gating=gating, components=components)
model.max_likelihood(obs, maxiter=500)
plt.figure()
model.plot(obs)
from mimo.distributions import GaussianWithDiagonalCovariance
from mimo.distributions import NormalGamma
from mimo.distributions import GaussianWithNormalGamma
from mimo.mixtures import MixtureOfGaussians
from mimo.mixtures import BayesianMixtureOfGaussians
npr.seed(1337)
gating = Categorical(K=2)
components = [GaussianWithDiagonalCovariance(mu=np.array([1., 1.]), sigmas=np.array([0.25, 0.5])),
GaussianWithDiagonalCovariance(mu=np.array([-1., -1.]), sigmas=np.array([0.5, 0.25]))]
gmm = MixtureOfGaussians(gating=gating, components=components)
obs, z = gmm.rvs(500)
gmm.plot(obs)
gating_hypparams = dict(K=2, alphas=np.ones((2, )))
gating_prior = Dirichlet(**gating_hypparams)
components_hypparams = dict(mu=np.zeros((2, )),
kappas=1e-2 * np.ones((2, )),
alphas=1. * np.ones((2, )),
betas=1. / (2. * 1e4) * np.ones((2, )))
components_prior = NormalGamma(**components_hypparams)
model = BayesianMixtureOfGaussians(gating=CategoricalWithDirichlet(gating_prior),
components=[GaussianWithNormalGamma(components_prior)