Fixed bernoulli likelihood divide by 0 and log of 0

GPy/inference/latent_function_inference/__init__.py

@@ -9,12 +9,12 @@ prior over a finite set of points f. This prior is
 where K is the kernel matrix.
 
 We also have a likelihood (see GPy.likelihoods) which defines how the data are
-related to the latent function: p(y | f).  If the likelihood is also a Gaussian, 
-the inference over f is tractable (see exact_gaussian_inference.py). 
+related to the latent function: p(y | f).  If the likelihood is also a Gaussian,
+the inference over f is tractable (see exact_gaussian_inference.py).
 
 If the likelihood object is something other than Gaussian, then exact inference
 is not tractable. We then resort to a Laplace approximation (laplace.py) or
-expectation propagation (ep.py). 
+expectation propagation (ep.py).
 
 The inference methods return a "Posterior" instance, which is a simple
 structure which contains a summary of the posterior. The model classes can then
@@ -24,7 +24,7 @@ etc.
 """
 
 from exact_gaussian_inference import ExactGaussianInference
-from laplace import LaplaceInference
+from laplace import Laplace
 expectation_propagation = 'foo' # TODO
 from dtc import DTC
 from fitc import FITC

GPy/inference/latent_function_inference/laplace.py

@@ -17,7 +17,7 @@ from posterior import Posterior
 import warnings
 from scipy import optimize
 
-class LaplaceInference(object):
+class Laplace(object):
 
     def __init__(self):
         """
@@ -52,6 +52,7 @@ class LaplaceInference(object):
 
         f_hat, Ki_fhat = self.rasm_mode(K, Y, likelihood, Ki_f_init, Y_metadata=Y_metadata)
 
+        self.f_hat = f_hat
         #Compute hessian and other variables at mode
         log_marginal, woodbury_vector, woodbury_inv, dL_dK, dL_dthetaL = self.mode_computations(f_hat, Ki_fhat, K, Y, likelihood, kern, Y_metadata)