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

GPy/core/parameterization/array_core.py

@@ -6,19 +6,6 @@ __updated__ = '2013-12-16'
 import numpy as np
 from parameter_core import Observable
 
-class ListArray(np.ndarray):
-    """
-    ndarray which can be stored in lists and checked if it is in.
-    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
-    #def __eq__(self, other):
-    #    return other is self
-
 class ParamList(list):
 
     def __contains__(self, other):
@@ -29,7 +16,7 @@ class ParamList(list):
 
     pass
 
-class ObservableArray(ListArray, Observable):
+class ObservableArray(np.ndarray, Observable):
     """
     An ndarray which reports changes to its observers.
     The observers can add themselves with a callable, which
@@ -46,6 +33,7 @@ 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:
@@ -54,8 +42,10 @@ class ObservableArray(ListArray, Observable):
         return self.__getitem__(slice(start, stop))
     def __setslice__(self, start, stop, val):
         return self.__setitem__(slice(start, stop), val)
+
     def __copy__(self, *args):
         return ObservableArray(self.view(np.ndarray).copy())
+
     def copy(self, *args):
         return self.__copy__(*args)
 
@@ -69,25 +59,21 @@ class ObservableArray(ListArray, Observable):
         self._notify_observers()
         return r
 
-
     def __irshift__(self, *args, **kwargs):
         r = np.ndarray.__irshift__(self, *args, **kwargs)
         self._notify_observers()
         return r
 
-
     def __rrshift__(self, *args, **kwargs):
         r = np.ndarray.__rrshift__(self, *args, **kwargs)
         self._notify_observers()
         return r
 
-
     def __ixor__(self, *args, **kwargs):
         r = np.ndarray.__ixor__(self, *args, **kwargs)
         self._notify_observers()
         return r
 
-
     def __rxor__(self, *args, **kwargs):
         r = np.ndarray.__rxor__(self, *args, **kwargs)
         self._notify_observers()

GPy/core/parameterization/param.py

@@ -46,6 +46,7 @@ class Param(ObservableArray, Constrainable, Gradcheckable):
     """
     __array_priority__ = -1 # Never give back Param
     _fixes_ = None
+    _parameters_ = []
     def __new__(cls, name, input_array, default_constraint=None):
         obj = numpy.atleast_1d(super(Param, cls).__new__(cls, input_array=input_array))
         obj._current_slice_ = (slice(obj.shape[0]),)
@@ -144,9 +145,6 @@ class Param(ObservableArray, Constrainable, Gradcheckable):
 #         from_name = self.name
 #         self.name = new_name
 #         self._direct_parent_._name_changed(self, from_name)
-    @property
-    def _parameters_(self):
-        return []
     def _collect_gradient(self, target):
         target[:] = self.gradient.flat
     #===========================================================================