for label in labels:
image = x_train[label]
x_dim, y_dim = image.shape
pixel_values = np.concatenate(image) # grey scale between 0 and 1
N, wmat, amat = create_matrices(x_dim, y_dim)
with pm.Model() as model:
beta0 = pm.Normal('beta0', mu=0., tau=1e-2)
tau = pm.Gamma('tau_c', alpha=1.0, beta=1.0)
mu_phi = CAR2('mu_phi', w=wmat, a=amat, tau=tau, shape=N)
phi = pm.Deterministic('phi', mu_phi -
tt.mean(mu_phi)) # zero-center phi
mu = pm.Deterministic('mu', beta0 + phi)
Yi = pm.LogitNormal('Yi', mu=mu, observed=pad(pixel_values))
max_a_post = pm.find_MAP()
step = pm.NUTS()
trace = pm.sample(draws=N_SAMPLES,
step=step,
start=max_a_post,
cores=2,
tune=N_TUNE,
chains=N_CHAINS)
posterior_pred = pm.sample_posterior_predictive(trace)
prefix_file_name = 'mnist_digit{}(label{})_'.format(i, label)
np.save(
new_name(name=prefix_file_name + 'phi_values',
def pymc3(self, name="theta"):
'''PyMC3 implementation of distribution'''
return pymc3.LogitNormal(name, mu=self.m, sd=self.s)