def test_gaussian_mixture_plot():
"""
Test the gaussian_mixture plotting function.
The code is from http://www.bayespy.org/examples/gmm.html
"""
np.random.seed(1)
y0 = np.random.multivariate_normal([0, 0], [[1, 0], [0, 0.02]], size=50)
y1 = np.random.multivariate_normal([0, 0], [[0.02, 0], [0, 1]], size=50)
y2 = np.random.multivariate_normal([2, 2], [[1, -0.9], [-0.9, 1]], size=50)
y3 = np.random.multivariate_normal([-2, -2], [[0.1, 0], [0, 0.1]], size=50)
y = np.vstack([y0, y1, y2, y3])
bpplt.pyplot.plot(y[:, 0], y[:, 1], 'rx')
N = 200
D = 2
K = 10
alpha = Dirichlet(1e-5 * np.ones(K), name='alpha')
Z = Categorical(alpha, plates=(N, ), name='z')
mu = Gaussian(np.zeros(D), 1e-5 * np.identity(D), plates=(K, ), name='mu')
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, scale=2)
def test_initialization(self):
"""
Test initialization of categorical nodes
"""
# Test initialization from random
with warnings.catch_warnings():
warnings.simplefilter("ignore", RuntimeWarning)
Z = Categorical([[0.0, 1.0, 0.0], [0.0, 0.0, 1.0]])
Z.initialize_from_random()
u = Z._message_to_child()
self.assertAllClose(u[0], [[0, 1, 0], [0, 0, 1]])
pass
def test_initialization(self):
"""
Test initialization of categorical nodes
"""
# Test initialization from random
with warnings.catch_warnings(record=True) as w:
Z = Categorical([[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0]])
Z.initialize_from_random()
u = Z._message_to_child()
self.assertAllClose(u[0],
[[0, 1, 0],
[0, 0, 1]])
pass
def test_gaussian_mixture_plot():
"""
Test the gaussian_mixture plotting function.
The code is from http://www.bayespy.org/examples/gmm.html
"""
np.random.seed(1)
y0 = np.random.multivariate_normal([0, 0], [[1, 0], [0, 0.02]], size=50)
y1 = np.random.multivariate_normal([0, 0], [[0.02, 0], [0, 1]], size=50)
y2 = np.random.multivariate_normal([2, 2], [[1, -0.9], [-0.9, 1]], size=50)
y3 = np.random.multivariate_normal([-2, -2], [[0.1, 0], [0, 0.1]], size=50)
y = np.vstack([y0, y1, y2, y3])
bpplt.pyplot.plot(y[:,0], y[:,1], 'rx')
N = 200
D = 2
K = 10
alpha = Dirichlet(1e-5*np.ones(K),
name='alpha')
Z = Categorical(alpha,
plates=(N,),
name='z')
mu = Gaussian(np.zeros(D), 1e-5*np.identity(D),
plates=(K,),
name='mu')
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, scale=2)
# Have to define these limits because on some particular environments these
# may otherwise differ and thus result in an image comparsion failure
bpplt.pyplot.xlim([-3, 6])
bpplt.pyplot.ylim([-3, 5])
def test_initialization(self):
"""
Test initialization of categorical nodes
"""
# Test initialization from random
with warnings.catch_warnings():
warnings.simplefilter("ignore", RuntimeWarning)
Z = Categorical([[0.0, 1.0, 0.0],
[0.0, 0.0, 1.0]])
Z.initialize_from_random()
u = Z._message_to_child()
self.assertAllClose(u[0],
[[0, 1, 0],
[0, 0, 1]])
pass
from bayespy.nodes import Dirichlet, Categorical from bayespy.nodes import Gaussian, Wishart from bayespy.nodes import Mixture from bayespy.inference import VB y0 = np.random.multivariate_normal([0, 0], [[2, 0], [0, 0.1]], size=50) y1 = np.random.multivariate_normal([0, 0], [[0.1, 0], [0, 2]], size=50) y2 = np.random.multivariate_normal([2, 2], [[2, -1.5], [-1.5, 2]], size=50) y3 = np.random.multivariate_normal([-2, -2], [[0.5, 0], [0, 0.5]], size=50) y = np.vstack([y0, y1, y2, y3]) N = 200 D = 2 K = 10 alpha = Dirichlet(1e-5*np.ones(K), name='alpha') Z = Categorical(alpha, plates=(N,),name='z') mu = Gaussian(np.zeros(D),1e-5*np.identity(D),plates=(K,),name='mu') 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)
