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GP model works now.

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Ricardo Andrade 2013-01-29 12:07:19 +00:00
parent d9a3226f49
commit 691aeeaf22
1 changed files with 5 additions and 31 deletions
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36
GPy/models/GP.py
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@ -13,7 +13,7 @@ from ..inference.likelihoods import likelihood,probit,poisson,gaussian
class GP(model):
"""
Gaussian Process model for regression
Gaussian Process model for regression and EP
:param X: input observations
:param Y: observed values
@ -35,7 +35,7 @@ class GP(model):
#TODO: make beta parameter explicit
#TODO: when using EP, predict needs to return 3 values otherwise it just needs 2. At the moment predict returns 3 values in any case.
def __init__(self,X,Y=None,kernel=None,normalize_X=False,normalize_Y=False, Xslices=None,likelihood=None,epsilon_ep=1e-3,epsilon_em=.1,power_ep=[1.,1.]):
def __init__(self,X,Y=None,kernel=None,normalize_X=False,normalize_Y=False, Xslices=None,likelihood=None,epsilon_ep=1e-3,power_ep=[1.,1.]):
# parse arguments
self.Xslices = Xslices
@ -121,6 +121,9 @@ class GP(model):
return self.kern._get_param_names_transformed()
def approximate_likelihood(self):
"""
Approximates a non-gaussian likelihood using Expectation Propagation
"""
assert not isinstance(self.likelihood, gaussian), "EP is only available for non-gaussian likelihoods"
self.ep_approx = Full(self.K,self.likelihood,epsilon = self.epsilon_ep,power_ep=[self.eta,self.delta])
self.beta, self.Y, self.Z_ep = self.ep_approx.fit_EP()
@ -170,7 +173,6 @@ class GP(model):
def predict(self,Xnew, slices=None, full_cov=False):
"""
Predict the function(s) at the new point(s) Xnew.
Arguments
@ -193,7 +195,6 @@ class GP(model):
If full_cov and self.D > 1, the return shape of var is Nnew x Nnew x self.D. If self.D == 1, the return shape is Nnew x Nnew.
This is to allow for different normalisations of the output dimensions.
"""
#normalise X values
@ -229,33 +230,6 @@ 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