name='X')
alpha = Gamma(1e-3, 1e-3,
plates=(D,),
name='alpha')
C = GaussianARD(0, alpha,
shape=(D,),
plates=(10,1),
name='C')
F = Dot(C, X)
tau = Gamma(1e-3, 1e-3, name='tau')
Y = GaussianARD(F, tau)
c = np.random.randn(10, 2)
x = np.random.randn(2, 100)
data = np.dot(c, x) + 0.1*np.random.randn(10, 100)
Y.observe(data)
Y.observe(data, mask=[[True], [False], [False], [True], [True],
[False], [True], [True], [True], [False]])
from bayespy.inference import VB
Q = VB(Y, C, X, alpha, tau)
X.initialize_from_parameters(np.random.randn(1, 100, D), 10)
from bayespy.inference.vmp import transformations
rotX = transformations.RotateGaussianARD(X)
rotC = transformations.RotateGaussianARD(C, alpha)
R = transformations.RotationOptimizer(rotC, rotX, D)
Q = VB(Y, C, X, alpha, tau)
Q.callback = R.rotate
Q.update(repeat=1000, tol=1e-6)
Q.update(repeat=50, tol=np.nan)
import bayespy.plot as bpplt
bpplt.pdf(Q['tau'], np.linspace(60, 140, num=100))
