def em_example(true_model, X, Z_true, mask):
# Fit a test model
model = FactorAnalysis(
D_obs, D_latent,
# W=true_model.W, sigmasq=true_model.sigmasq
)
inf_data = model.add_data(X, mask=mask)
model.set_empirical_mean()
lps = []
angles = []
N_iters = 100
for _ in progprint_xrange(N_iters):
model.EM_step()
lps.append(model.log_likelihood())
angles.append(principal_angle(true_model.W, model.W))
plot_results(lps, angles, Z_true, inf_data.E_Z)
def gibbs_example(true_model, X, Z_true, mask):
# Fit a test model
model = FactorAnalysis(
D_obs,
D_latent,
# W=true_model.W, sigmasq=true_model.sigmasq
)
inf_data = model.add_data(X, mask=mask)
model.set_empirical_mean()
lps = []
angles = []
N_iters = 100
for _ in progprint_xrange(N_iters):
model.resample_model()
lps.append(model.log_likelihood())
angles.append(principal_angle(true_model.W, model.W))
plot_results(lps, angles, Z_true, inf_data.Z)
def meanfield_example(true_model, X, Z_true, mask):
# Fit a test model
model = FactorAnalysis(
D_obs, D_latent,
# W=true_model.W, sigmasq=true_model.sigmasq
)
inf_data = model.add_data(X, mask=mask)
model.set_empirical_mean()
lps = []
angles = []
N_iters = 100
for _ in progprint_xrange(N_iters):
model.meanfield_coordinate_descent_step()
lps.append(model.expected_log_likelihood())
E_W, _, _, _ = model.regression.mf_expectations
angles.append(principal_angle(true_model.W, E_W))
plot_results(lps, angles, Z_true, inf_data.Z)
def meanfield_example(true_model, X, Z_true, mask):
# Fit a test model
model = FactorAnalysis(
D_obs,
D_latent,
# W=true_model.W, sigmasq=true_model.sigmasq
)
inf_data = model.add_data(X, mask=mask)
model.set_empirical_mean()
lps = []
angles = []
N_iters = 100
for _ in progprint_xrange(N_iters):
model.meanfield_coordinate_descent_step()
lps.append(model.expected_log_likelihood())
E_W, _, _, _ = model.regression.mf_expectations
angles.append(principal_angle(true_model.W, E_W))
plot_results(lps, angles, Z_true, inf_data.Z)
