Fixed more errors in docs 2

GPy/likelihoods/ep.py

@@ -95,6 +95,7 @@ class EP(likelihood):
         :type epsilon: float
         :param power_ep: Power EP parameters
         :type power_ep: list of floats
+
         """
         self.epsilon = epsilon
         self.eta, self.delta = power_ep
@@ -165,6 +166,7 @@ class EP(likelihood):
         :type epsilon: float
         :param power_ep: Power EP parameters
         :type power_ep: list of floats
+
         """
         self.epsilon = epsilon
         self.eta, self.delta = power_ep

GPy/likelihoods/likelihood.py

@@ -10,14 +10,16 @@ class likelihood(Parameterized):
     (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
-    self.V : self.precision * self.Y
+
+        - 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
+        - self.V : self.precision * self.Y
+
     """
     def __init__(self):
         Parameterized.__init__(self)