very basic functionality is now working

GPy/likelihoods/Gaussian.py

@@ -1,16 +1,39 @@
 import numpy as np
 
 class Gaussian:
-    def __init__(self,data,variance=1.,normalise=False):
+    def __init__(self,data,variance=1.,normalize=False):
         self.data = data
-        if normalise:
-            foo
-        self._variance = variance
+        self.N,D = data.shape
+        self.Z = 0. # a correction factor which accounts for the approximation made
+
+        #normalisation
+        if normalize:
+            self._mean = data.mean(0)[None,:]
+            self._std = data.std(0)[None,:]
+            self.Y = (self.data - self._mean)/self._std
+        else:
+            self._mean = np.zeros((1,D))
+            self._std = np.ones((1,D))
+            self.Y = self.data
+
+        self.YYT = np.dot(self.Y,self.Y.T)
+        self._set_params(np.asarray(variance))
+
     def _get_params(self):
-        return np.asarray(self.variance)
+        return np.asarray(self._variance)
+
+    def _get_param_names(self):
+        return ["noise variance"]
+
     def _set_params(self,x):
         self._variance = x
+        self.variance = np.eye(self.N)*self._variance
+
     def fit(self):
+        """
+        No approximations needed
+        """
         pass
-    def _gradients(self,foo):
-        return bar(foo)
+
+    def _gradients(self,partial):
+        return np.sum(np.diag(partial))