toy_example_bmm.py

p0 = [0.1, 0.9, 0.1, 0.9, 0.1, 0.9, 0.1, 0.9, 0.1, 0.9]
p1 = [0.1, 0.1, 0.1, 0.1, 0.1, 0.9, 0.9, 0.9, 0.9, 0.9]
p2 = [0.9, 0.9, 0.9, 0.9, 0.9, 0.1, 0.1, 0.1, 0.1, 0.1]

import numpy as np
p = np.array([p0, p1, p2])

from bayespy.utils import random
z = random.categorical([1/3, 1/3, 1/3], size=100)

x = random.bernoulli(p[z])

#N = 100
#D = 10
#K = 10


#N = access.shape[0]
N=access.as_matrix().transpose().astype(bool).shape[0]

#D = access.shape[1]
D=access.as_matrix().transpose().astype(bool).shape[1]

#K = access.shape[0]
K=access.as_matrix().transpose().astype(bool).shape[0]


from bayespy.nodes import Categorical, Dirichlet
R = Dirichlet(K*[1e-5], name='R')
Z = Categorical(R, plates=(N,1), name='Z')

from bayespy.nodes import Beta
P = Beta([0.5, 0.5], plates=(D,K), name='P')

from bayespy.nodes import Mixture, Bernoulli
X = Mixture(Z, Bernoulli, P)

from bayespy.inference import VB
Q = VB(Z, R, X, P)

P.initialize_from_random()

#X.observe(x)
#X.observe(access.as_matrix())
X.observe(access.as_matrix().transpose().astype(bool))

Q.update(repeat=1000)

import bayespy.plot as bpplt
bpplt.hinton(R)


bpplt.hinton(P)

bpplt.hinton(Z)

bpplt.pyplot.show()