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EM algorithm

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Ricardo Andrade 2013-01-28 18:01:55 +00:00
parent 29ec128c9d
commit 7737cecf6d
2 changed files with 28 additions and 0 deletions
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27
GPy/models/GP.py
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@ -229,6 +229,33 @@ class GP(model):
phi = None if not self.EP else self.likelihood.predictive_mean(mu,var)
return mu, var, phi
def EM(self,max_f_eval=20,epsilon=.1,plot_all=False): #TODO check this makes sense
"""
Fits sparse_EP and optimizes the hyperparametes iteratively until convergence is achieved.
"""
self.epsilon_em = epsilon
log_likelihood_change = self.epsilon_em + 1.
self.parameters_path = [self._get_params()]
self.approximate_likelihood()
self.site_approximations_path = [[self.ep_approx.tau_tilde,self.ep_approx.v_tilde]]
self.log_likelihood_path = [self.log_likelihood()]
iteration = 0
while log_likelihood_change > self.epsilon_em:
print 'EM iteration', iteration
self.optimize(max_f_eval = max_f_eval)
log_likelihood_new = self.log_likelihood()
log_likelihood_change = log_likelihood_new - self.log_likelihood_path[-1]
if log_likelihood_change < 0:
print 'log_likelihood decrement'
self._set_params(self.parameters_path[-1])
self.kern._set_params(self.parameters_path[-1])
else:
self.approximate_likelihood()
self.log_likelihood_path.append(self.log_likelihood())
self.parameters_path.append(self._get_params())
self.site_approximations_path.append([self.ep_approx.tau_tilde,self.ep_approx.v_tilde])
iteration += 1
def plot(self,samples=0,plot_limits=None,which_data='all',which_functions='all',resolution=None):
"""
:param samples: the number of a posteriori samples to plot