checkgrad now global and callable from any parameter

GPy/core/model.py

@@ -380,7 +380,7 @@ class Model(Parameterized):
         sgd.run()
         self.optimization_runs.append(sgd)
 
-    def checkgrad(self, target_param=None, verbose=False, step=1e-6, tolerance=1e-3):
+    def _checkgrad(self, target_param=None, verbose=False, step=1e-6, tolerance=1e-3):
         """
         Check the gradient of the ,odel by comparing to a numerical
         estimate.  If the verbose flag is passed, invividual
@@ -434,7 +434,7 @@ class Model(Parameterized):
             if target_param is None:
                 param_list = range(len(x))
             else:
-                param_list = self.grep_param_names(target_param, transformed=True, search=True)
+                param_list = self._raveled_index_for(target_param)
                 if not np.any(param_list):
                     print "No free parameters to check"
                     return

GPy/core/parameterization/param.py

@@ -3,8 +3,8 @@
 
 import itertools
 import numpy
-from parameter_core import Constrainable, adjust_name_for_printing
-from array_core import ObservableArray
+from parameter_core import Constrainable, Gradcheckable, adjust_name_for_printing
+from array_core import ObservableArray, ParamList
 
 ###### printing
 __constraints_name__ = "Constraint"
@@ -20,7 +20,7 @@ class Float(numpy.float64, Constrainable):
         self._base = base
 
 
-class Param(ObservableArray, Constrainable):
+class Param(ObservableArray, Constrainable, Gradcheckable):
     """
     Parameter object for GPy models.
 
@@ -547,6 +547,11 @@ class ParamConcatenation(object):
 
     def untie(self, *ties):
         [param.untie(*ties) for param in self.params]
+
+    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/core/parameterization/parameter_core.py

@@ -52,7 +52,6 @@ class Parentable(object):
         super(Parentable,self).__init__()        
         self._direct_parent_ = direct_parent
         self._parent_index_ = parent_index
-        self._highest_parent_ = highest_parent
 
     def has_parent(self):
         return self._direct_parent_ is not None
@@ -77,7 +76,19 @@ class Nameable(Parentable):
         from_name = self.name
         self._name = name
         if self.has_parent():
-            self._direct_parent_._name_changed(self, from_name)
+            self._direct_parent_._name_changed(self, from_name)    
+
+class Gradcheckable(Parentable):
+    #===========================================================================
+    # Gradchecking
+    #===========================================================================
+    def checkgrad(self, verbose=0, step=1e-6, tolerance=1e-3):
+        if self.has_parent():
+            return self._highest_parent_._checkgrad(self, verbose=verbose, step=step, tolerance=tolerance)
+        return self._checkgrad(self[''], verbose=verbose, step=step, tolerance=tolerance)
+    def _checkgrad(self, param):
+        raise NotImplementedError, "Need log likelihood to check gradient against"
+
 
 class Constrainable(Nameable):
     def __init__(self, name):

GPy/core/parameterization/parameterized.py

@@ -8,7 +8,7 @@ import cPickle
 import itertools
 from re import compile, _pattern_type
 from param import ParamConcatenation, Param
-from parameter_core import Constrainable, Pickleable, Observable, adjust_name_for_printing
+from parameter_core import Constrainable, Pickleable, Observable, adjust_name_for_printing, Gradcheckable
 from index_operations import ParameterIndexOperations,\
     index_empty
 from array_core import ParamList
@@ -24,7 +24,7 @@ FIXED = False
 UNFIXED = True
 #===============================================================================
 
-class Parameterized(Constrainable, Pickleable, Observable):
+class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
     """
     Parameterized class
 
@@ -230,7 +230,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
             elif not (pname in not_unique):
                 self.__dict__[pname] = p
                 self._added_names_.add(pname)
-        
+            
     #===========================================================================
     # Pickling operations
     #===========================================================================
@@ -385,7 +385,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
         """
         return numpy.r_[:self.size]
     #===========================================================================
-    # Handle ties:
+    # Fixing parameters:
     #===========================================================================
     def _set_fixed(self, param_or_index):
         if not self._has_fixes(): self._fixes_ = numpy.ones(self.size, dtype=bool)
@@ -410,9 +410,6 @@ class Parameterized(Constrainable, Pickleable, Observable):
         if self._has_fixes():
             return self._fixes_[self._raveled_index_for(param)]
         return numpy.ones(self.size, dtype=bool)[self._raveled_index_for(param)]
-    #===========================================================================
-    # Fixing parameters:
-    #===========================================================================
     def _fix(self, param, warning=True):
         f = self._add_constrain(param, __fixed__, warning)
         self._set_fixed(f)