Merge branch 'devel' of github.com:SheffieldML/GPy into devel

GPy/core/parameterized.py

@@ -372,6 +372,9 @@ class Parameterized(object):
             for j in tie:
                 ties[j] = '(' + str(i) + ')'
 
+        if values.size == 1:
+            values = ['%.4f' %float(values)]
+        else:
             values = ['%.4f' % float(v) for v in values]
         max_names = max([len(names[i]) for i in range(len(names))] + [len(header[0])])
         max_values = max([len(values[i]) for i in range(len(values))] + [len(header[1])])

GPy/likelihoods/ep.py

@@ -37,6 +37,8 @@ class EP(likelihood):
         self.VVT_factor = self.V
         self.trYYT = 0.
 
+        super(EP, self).__init__()
+
     def restart(self):
         self.tau_tilde = np.zeros(self.N)
         self.v_tilde = np.zeros(self.N)

GPy/likelihoods/gaussian.py

@@ -34,6 +34,8 @@ class Gaussian(likelihood):
         self._variance = np.asarray(variance) + 1.
         self._set_params(np.asarray(variance))
 
+        super(Gaussian, self).__init__()
+
     def set_data(self, data):
         self.data = data
         self.N, D = data.shape

GPy/likelihoods/gaussian_mixed_noise.py

@@ -45,6 +45,8 @@ class Gaussian_Mixed_Noise(likelihood):
         self.set_data(data_list)
         self._set_params(np.asarray(noise_params))
 
+        super(Gaussian_Mixed_Noise, self).__init__()
+
     def set_data(self, data_list):
         self.data = np.vstack(data_list)
         self.N, D = self.data.shape

GPy/likelihoods/likelihood.py

@@ -1,7 +1,8 @@
 import numpy as np
 import copy
+from ..core.parameterized import Parameterized
 
-class likelihood:
+class likelihood(Parameterized):
     """
     The atom for a likelihood class
 
@@ -18,8 +19,8 @@ class likelihood:
     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,data):
+    def __init__(self):
-        raise ValueError, "this class is not to be instantiated"
+        Parameterized.__init__(self)
 
     def _get_params(self):
         raise NotImplementedError
@@ -38,7 +39,3 @@ class likelihood:
 
     def predictive_values(self, mu, var):
         raise NotImplementedError
-
-    def copy(self):
-        """ Returns a (deep) copy of the current likelihood """
-        return copy.deepcopy(self)

GPy/likelihoods/noise_model_constructors.py

@@ -42,7 +42,7 @@ def exponential(gp_link=None):
     analytical_variance = False
     return noise_models.exponential_noise.Exponential(gp_link,analytical_mean,analytical_variance)
 
-def gaussian(gp_link=None,variance=1.):
+def gaussian_ep(gp_link=None,variance=1.):
     """
     Construct a gaussian likelihood