def update_mixture(self, mixture, increase_tolerance=1e-6):
# update mixture
convergence_test = LlConvergenceTest(eps=1e-4, should_increase=True, use_absolute_difference=False)
last_LL = None
logging.info('Variational bound at start = %f', mixture.variational_bound())
for _i in xrange(self.options.max_iter):
if None == last_LL:
last_LL = mixture.variational_bound_piecewise()
convergence_test(last_LL.sum())
mixture.q_z.update(mixture)
last_LL = check_LL_increased(last_LL, mixture.variational_bound_piecewise(), tag="Update z", tolerance=increase_tolerance, raise_error=True)
assert np.isfinite(last_LL).all(), str(last_LL)
mixture._check_shapes()
mixture.reorder_components()
last_LL = check_LL_increased(last_LL, mixture.variational_bound_piecewise(), tag="Reorder components", tolerance=increase_tolerance, raise_error=True)
assert np.isfinite(last_LL).all(), str(last_LL)
mixture._check_shapes()
if hasattr(mixture, 'q_pi'):
mixture.q_pi.update(mixture)
assert .99 < mixture.q_pi.E().sum() < 1.01
last_LL = check_LL_increased(last_LL, mixture.variational_bound_piecewise(), tag="Update pi", tolerance=increase_tolerance, raise_error=True)
assert np.isfinite(last_LL).all(), str(last_LL)
mixture._check_shapes()
mixture.q_eta.update(mixture)
last_LL = check_LL_increased(last_LL, mixture.variational_bound_piecewise(), tag="Update eta", tolerance=increase_tolerance, raise_error=False)
assert np.isfinite(last_LL).all(), str(last_LL)
mixture._check_shapes()
logging.info('Iteration %d: variational bound = %f', _i+1, mixture.variational_bound())
if _i + 1 >= self.options.min_iter and convergence_test(last_LL.sum()):
logging.info('Variational bound has converged : stopping.')
break
