import numpy.random as npr
from matplotlib import pyplot as plt
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)
import numpy as np
import numpy.random as npr
from mimo.distributions import GaussianWithCovariance
from mimo.distributions import GaussianWithPrecision
from mimo.distributions import NormalWishart
from mimo.distributions import GaussianWithNormalWishart
npr.seed(1337)
dim, nb_samples, nb_datasets = 3, 500, 5
dist = GaussianWithCovariance(mu=npr.randn(dim),
sigma=1. * np.diag(npr.rand(dim)))
data = [dist.rvs(size=nb_samples) for _ in range(nb_datasets)]
print("True mean" + "\n", dist.mu.T, "\n" + "True sigma" + "\n", dist.sigma)
model = GaussianWithPrecision(mu=np.zeros((dim, )))
model.max_likelihood(data)
print("ML mean" + "\n", model.mu.T, "\n" + "ML sigma" + "\n", model.sigma)
hypparams = dict(mu=np.zeros((dim, )), kappa=0.01, psi=np.eye(dim), nu=dim + 1)
prior = NormalWishart(**hypparams)
model = GaussianWithNormalWishart(prior=prior)
model.max_aposteriori(data)
print("MAP mean" + "\n", model.likelihood.mu.T, "\n" + "MAP sigma" + "\n",
model.likelihood.sigma)