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

GPy/core/parameterization/array_core.py

@@ -12,7 +12,7 @@ class ListArray(np.ndarray):
     WARNING: This overrides the functionality of x==y!!!
     Use numpy.equal(x,y) for element-wise equality testing.
     """
-    
+
     def __new__(cls, input_array):
         obj = np.asanyarray(input_array).view(cls)
         return obj
@@ -26,7 +26,7 @@ class ParamList(list):
             if el is other:
                 return True
         return False
-    
+
     pass
 
 class ObservableArray(ListArray, Observable):
@@ -46,7 +46,6 @@ class ObservableArray(ListArray, Observable):
         # see InfoArray.__array_finalize__ for comments
         if obj is None: return
         self._observers_ = getattr(obj, '_observers_', None)
-    
     def __setitem__(self, s, val, update=True):
         super(ObservableArray, self).__setitem__(s, val)
         if update:
@@ -54,10 +53,9 @@ class ObservableArray(ListArray, Observable):
     def __getslice__(self, start, stop):
         return self.__getitem__(slice(start, stop))
     def __setslice__(self, start, stop, val):
-        return self.__setitem__(slice(start, stop), val)  
-
+        return self.__setitem__(slice(start, stop), val)
     def __copy__(self, *args):
-        return ObservableArray(self.base.base.copy(*args))
+        return ObservableArray(self.view(np.ndarray).copy())
     def copy(self, *args):
         return self.__copy__(*args)
 
@@ -65,7 +63,6 @@ class ObservableArray(ListArray, Observable):
         r =  np.ndarray.__ror__(self, *args, **kwargs)
         self._notify_observers()
         return r
-        
 
     def __ilshift__(self, *args, **kwargs):
         r = np.ndarray.__ilshift__(self, *args, **kwargs)

GPy/core/parameterization/param.py

@@ -154,14 +154,14 @@ class Param(ObservableArray, Constrainable, Gradcheckable):
     #===========================================================================
     def tie_to(self, param):
         """
-        :param param: the parameter object to tie this parameter to. 
+        :param param: the parameter object to tie this parameter to.
                       Can be ParamConcatenation (retrieved by regexp search)
-        
+
         Tie this parameter to the given parameter.
         Broadcasting is not allowed, but you can tie a whole dimension to
         one parameter:  self[:,0].tie_to(other), where other is a one-value
         parameter.
-        
+
         Note: For now only one parameter can have ties, so all of a parameter
               will be removed, when re-tieing!
         """
@@ -531,7 +531,7 @@ class ParamConcatenation(object):
     def checkgrad(self, verbose=0, step=1e-6, tolerance=1e-3):
         return self.params[0]._highest_parent_._checkgrad(self, verbose, step, tolerance)
     #checkgrad.__doc__ = Gradcheckable.checkgrad.__doc__
-    
+
     __lt__ = lambda self, val: self._vals() < val
     __le__ = lambda self, val: self._vals() <= val
     __eq__ = lambda self, val: self._vals() == val

GPy/inference/latent_function_inference/laplace.py

@@ -92,12 +92,11 @@ class LaplaceInference(object):
         iteration = 0
         while difference > self._mode_finding_tolerance and iteration < self._mode_finding_max_iter:
             W = -likelihood.d2logpdf_df2(f, Y, extra_data=Y_metadata)
-
-            W_f = W*f
             grad = likelihood.dlogpdf_df(f, Y, extra_data=Y_metadata)
 
+            W_f = W*f
+
             b = W_f + grad # R+W p46 line 6.
-            #W12BiW12Kb, B_logdet = self._compute_B_statistics(K, W.copy(), np.dot(K, b), likelihood.log_concave)
             W12BiW12, _, _ = self._compute_B_statistics(K, W, likelihood.log_concave)
             W12BiW12Kb = np.dot(W12BiW12, np.dot(K, b))
 

GPy/testing/likelihood_tests.py

@@ -1,10 +1,10 @@
 import numpy as np
 import unittest
 import GPy
-from GPy.models import GradientChecker
+from ..models import GradientChecker
 import functools
 import inspect
-from GPy.likelihoods import link_functions
+from ..likelihoods import link_functions
 from ..core.parameterization import Param
 from functools import partial
 #np.random.seed(300)