added a likelihood atom class

and also some import tidying in the EP.py file

GPy/likelihoods/EP.py

@@ -1,11 +1,9 @@
 import numpy as np
-import random
 from scipy import stats, linalg
-from ..core import model
 from ..util.linalg import pdinv,mdot,jitchol
-from ..util.plot import gpplot
+from likelihood import likelihood
 
-class EP:
+class EP(likelihood):
     def __init__(self,data,likelihood_function,epsilon=1e-3,power_ep=[1.,1.]):
         """
         Expectation Propagation
@@ -70,8 +68,7 @@ class EP:
         self.np1 = [self.tau_tilde.copy()]
         self.np2 = [self.v_tilde.copy()]
         while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:
-            update_order = np.arange(self.N)
+            update_order = np.random.permutation(self.N)
-            random.shuffle(update_order)
             for i in update_order:
                 #Cavity distribution parameters
                 self.tau_[i] = 1./self.Sigma[i,i] - self.eta*self.tau_tilde[i]
@@ -243,8 +240,7 @@ class EP:
         self.np1 = [self.tau_tilde.copy()]
         self.np2 = [self.v_tilde.copy()]
         while epsilon_np1 > self.epsilon or epsilon_np2 > self.epsilon:
-            update_order = np.arange(self.N)
+            update_order = np.random.permutation(self.N)
-            random.shuffle(update_order)
             for i in update_order:
                 #Cavity distribution parameters
                 self.tau_[i] = 1./self.Sigma_diag[i] - self.eta*self.tau_tilde[i]

GPy/likelihoods/Gaussian.py

@@ -1,6 +1,7 @@
 import numpy as np
+from likelihood import likelihood
 
-class Gaussian:
+class Gaussian(likelihood):
     def __init__(self,data,variance=1.,normalize=False):
         self.is_heteroscedastic = False
         self.data = data

GPy/likelihoods/likelihood.py

@@ -0,0 +1,35 @@
+import numpy as np
+
+class likelihood:
+    """
+    The atom for a likelihood class
+
+    This object interfaces the GP and the data. The most basic likelihood
+    (Gaussian) inherits directly from this, as does the EP algorithm
+
+    Some things must be defined for this to work properly:
+    self.Y : the effective Gaussian target of the GP
+    self.N, self.D : Y.shape
+    self.covariance_matrix : the effective (noise) covariance of the GP targets
+    self.Z : a factor which gets added to the likelihood (0 for a Gaussian, Z_EP for EP)
+    self.is_heteroscedastic : enables significant computational savings in GP
+    self.precision : a scalar or vector representation of the effective target precision
+    self.YYT : (optional) = np.dot(self.Y, self.Y.T) enables computational savings for D>N
+    """
+    def __init__(self,data):
+        raise ValueError, "this class is not to be instantiated"
+
+    def _get_params(self):
+        raise NotImplementedError
+
+    def _get_param_names(self):
+        raise NotImplementedError
+
+    def _set_params(self,x):
+        raise NotImplementedError
+
+    def fit(self):
+        raise NotImplementedError
+
+    def _gradients(self,partial):
+        raise NotImplementedError