some tifying in the models classes

GPy/likelihoods/bernoulli.py

@@ -22,8 +22,12 @@ class Bernoulli(Likelihood):
     .. See also::
         likelihood.py, for the parent class
     """
-    def __init__(self, gp_link=None, analytical_mean=False, analytical_variance=False):
+    def __init__(self, gp_link=None):
-        super(Bernoulli, self).__init__(gp_link, analytical_mean, analytical_variance)
+        if gp_link is None:
+            gp_link = link_functions.Probit()
+
+        super(Bernoulli, self).__init__(gp_link, 'Bernoulli')
+
         if isinstance(gp_link , (link_functions.Heaviside, link_functions.Probit)):
             self.log_concave = True
 

GPy/models/gp_classification.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2013, Ricardo Andrade
+# Copyright (c) 2013, the GPy Authors (see AUTHORS.txt)
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-
 import numpy as np
 from ..core import GP
 from .. import likelihoods
@@ -15,27 +15,16 @@ class GPClassification(GP):
 
     :param X: input observations
     :param Y: observed values, can be None if likelihood is not None
-    :param likelihood: a GPy likelihood, defaults to Binomial with probit link_function
     :param kernel: a GPy kernel, defaults to rbf
-    :param normalize_X:  whether to normalize the input data before computing (predictions will be in original scales)
-    :type normalize_X: False|True
-    :param normalize_Y:  whether to normalize the input data before computing (predictions will be in original scales)
-    :type normalize_Y: False|True
 
     .. Note:: Multiple independent outputs are allowed using columns of Y
 
     """
 
-    def __init__(self,X,Y=None,likelihood=None,kernel=None,normalize_X=False,normalize_Y=False):
+    def __init__(self, X, Y, kernel=None):
         if kernel is None:
             kernel = kern.rbf(X.shape[1])
 
-        if likelihood is None:
+        likelihood = likelihoods.Bernoulli()
-            noise_model = likelihoods.binomial()
-            likelihood = likelihoods.EP(Y, noise_model)
-        elif Y is not None:
-            if not all(Y.flatten() == likelihood.data.flatten()):
-                raise Warning, 'likelihood.data and Y are different.'
 
-        GP.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
+        GP.__init__(self, X=X, Y=Y,  kernel=kernel, likelihood=likelihood, name='gp_classification')
-        self.ensure_default_constraints()

GPy/models/gp_multioutput_regression.py

@@ -1,7 +1,6 @@
 # Copyright (c) 2013, Ricardo Andrade
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-
 import numpy as np
 from ..core import GP
 from .. import likelihoods

GPy/models/gp_regression.py

@@ -22,6 +22,7 @@ class GPRegression(GP):
     """
 
     def __init__(self, X, Y, kernel=None):
+
         if kernel is None:
             kernel = kern.rbf(X.shape[1])
 

GPy/models/gradient_checker.py

@@ -1,8 +1,6 @@
-'''
+# ## Copyright (c) 2012, GPy authors (see AUTHORS.txt).
-Created on 17 Jul 2013
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-@author: maxz
-'''
 from GPy.core.model import Model
 import itertools
 import numpy

GPy/models/mrd.py

@@ -1,8 +1,6 @@
-'''
+# ## Copyright (c) 2013, GPy authors (see AUTHORS.txt).
-Created on 10 Apr 2013
+# Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-@author: Max Zwiessele
-'''
 from GPy.core import Model
 from GPy.core import SparseGP
 from GPy.util.linalg import PCA