some tidying in the likelihood classes

GPy/likelihoods/EP.py

@@ -18,12 +18,10 @@ class EP:
         self.likelihood_function = likelihood_function
         self.epsilon = epsilon
         self.eta, self.delta = power_ep
-        self.jitter = 1e-12 # TODO: is this needed?
+        self.is_heteroscedastic = True
 
-        """
-        Initial values - Likelihood approximation parameters:
-        p(y|f) = t(f|tau_tilde,v_tilde)
-        """
+        #Initial values - Likelihood approximation parameters:
+        #p(y|f) = t(f|tau_tilde,v_tilde)
         self.tau_tilde = np.zeros(self.N)
         self.v_tilde = np.zeros(self.N)
 
@@ -32,8 +30,11 @@ class EP:
         mu_tilde = self.v_tilde/self.tau_tilde #When calling EP, this variable is used instead of Y in the GP model
         sigma_sum = 1./self.tau_ + 1./self.tau_tilde
         mu_diff_2 = (self.v_/self.tau_ - mu_tilde)**2
-        Z_ep = np.sum(np.log(self.Z_hat)) + 0.5*np.sum(np.log(sigma_sum)) + 0.5*np.sum(mu_diff_2/sigma_sum) #Normalization constant
-        self.Y, self.beta, self.Z = self.tau_tilde[:,None], mu_tilde[:,None], Z_ep
+        self.Z = np.sum(np.log(self.Z_hat)) + 0.5*np.sum(np.log(sigma_sum)) + 0.5*np.sum(mu_diff_2/sigma_sum) #Normalization constant, aka Z_ep
+
+        self.Y =  mu_tilde[:,None]
+        self.precsion = self.tau_tilde
+        self.covariance_matrix = np.diag(1./self.precision)
 
     def fit_full(self,K):
         """

GPy/likelihoods/Gaussian.py

@@ -2,6 +2,7 @@ import numpy as np
 
 class Gaussian:
     def __init__(self,data,variance=1.,normalize=False):
+        self.is_heteroscedastic = False
         self.data = data
         self.N,D = data.shape
         self.Z = 0. # a correction factor which accounts for the approximation made
@@ -19,6 +20,7 @@ class Gaussian:
         self.YYT = np.dot(self.Y,self.Y.T)
         self._set_params(np.asarray(variance))
 
+
     def _get_params(self):
         return np.asarray(self._variance)
 
@@ -27,7 +29,8 @@ class Gaussian:
 
     def _set_params(self,x):
         self._variance = x
-        self.variance = np.eye(self.N)*self._variance
+        self.covariance_matrix = np.eye(self.N)*self._variance
+        self.precision = 1./self._variance
 
     def fit(self):
         """

GPy/likelihoods/likelihood_functions.py

@@ -8,18 +8,18 @@ import scipy as sp
 import pylab as pb
 from ..util.plot import gpplot
 
-class likelihood:
+class likelihood_function:
     """
     Likelihood class for doing Expectation propagation
 
     :param Y: observed output (Nx1 numpy.darray)
-    ..Note:: Y values allowed depend on the likelihood used
+    ..Note:: Y values allowed depend on the likelihood_function used
     """
     def __init__(self,location=0,scale=1):
         self.location = location
         self.scale = scale
 
-class probit(likelihood):
+class probit(likelihood_function):
     """
     Probit likelihood
     Y is expected to take values in {-1,1}
@@ -29,7 +29,7 @@ class probit(likelihood):
     $$
     """
     def __init__(self,location=0,scale=1):
-        likelihood.__init__(self,Y,location,scale)
+        likelihood_function.__init__(self,Y,location,scale)
 
     def moments_match(self,data_i,tau_i,v_i):
         """
@@ -64,7 +64,7 @@ class probit(likelihood):
     def _log_likelihood_gradients():
         return np.zeros(0) # there are no parameters of whcih to compute the gradients
 
-class poisson(likelihood):
+class poisson(likelihood_function):
     """
     Poisson likelihood
     Y is expected to take values in {0,1,2,...}
@@ -75,7 +75,7 @@ class poisson(likelihood):
     """
     def __init__(self,Y,location=0,scale=1):
         assert len(Y[Y<0]) == 0, "Output cannot have negative values"
-        likelihood.__init__(self,Y,location,scale)
+        likelihood_function.__init__(self,Y,location,scale)
 
     def moments_match(self,i,tau_i,v_i):
         """
@@ -160,7 +160,7 @@ class poisson(likelihood):
         if Z is not None:
             pb.plot(Z,Z*0+pb.ylim()[0],'k|',mew=1.5,markersize=12)
 
-class gaussian(likelihood):
+class gaussian(likelihood_function):
     """
     Gaussian likelihood
     Y is expected to take values in (-inf,inf)