[copy] handled hierarchy error for copying

GPy/core/parameterization/lists_and_dicts.py

@@ -59,6 +59,7 @@ class ObservablesList(object):
         return self._poc.__repr__()
 
     def add(self, priority, observable, callble):
+        if observable is not None:
             ins = 0
             for pr, _, _ in self:
                 if priority > pr:
@@ -96,8 +97,10 @@ class ObservablesList(object):
     def __deepcopy__(self, memo):
         self.flush()
         s = ObservablesList()
+        for p,o,c in self._poc:
             import copy
-        s._poc = copy.deepcopy(self._poc, memo)
+            s.add(p, copy.deepcopy(o(), memo), copy.deepcopy(c, memo))
+        s.flush()
         return s
 
     def __getstate__(self):

GPy/core/parameterization/param.py

@@ -156,6 +156,13 @@ class Param(OptimizationHandlable, ObsAr):
     def _ensure_fixes(self):
         if not self._has_fixes(): self._fixes_ = numpy.ones(self._realsize_, dtype=bool)
 
+    #===========================================================================
+    # parameterizable
+    #===========================================================================
+    def traverse(self, visit, *args, **kwargs):
+        visit(self, *args, **kwargs)
+
+
     #===========================================================================
     # Convenience
     #===========================================================================

GPy/core/parameterization/parameter_core.py

@@ -17,7 +17,7 @@ from transformations import Logexp, NegativeLogexp, Logistic, __fixed__, FIXED,
 import numpy as np
 import re
 
-__updated__ = '2014-04-16'
+__updated__ = '2014-05-12'
 
 class HierarchyError(Exception):
     """
@@ -124,7 +124,7 @@ class Parentable(object):
         """
         Disconnect this object from its parent
         """
-        raise NotImplementedError, "Abstaract superclass"
+        raise NotImplementedError, "Abstract superclass"
 
     @property
     def _highest_parent_(self):
@@ -162,7 +162,6 @@ class Pickleable(object):
         :param protocol: pickling protocol to use, python-pickle for details.
         """
         import cPickle as pickle
-        import pickle #TODO: cPickle
         if isinstance(f, str):
             with open(f, 'w') as f:
                 pickle.dump(self, f, protocol)
@@ -177,7 +176,12 @@ class Pickleable(object):
         #raise NotImplementedError, "Copy is not yet implemented, TODO: Observable hierarchy"
         import copy
         memo = {}
-        memo[id(self._parent_)] = None
+        parents = []
+        self.traverse_parents(parents.append)
+        # remove self, which is the first arguments
+        parents = [p for p in parents if p is not self]
+        for p in parents:
+            memo[id(p)] = None
         memo[id(self.gradient)] = None
         memo[id(self.param_array)] = None
         memo[id(self._fixes_)] = None
@@ -202,9 +206,6 @@ class Pickleable(object):
         dc = dict()
         for k,v in self.__dict__.iteritems():
             if k not in ignore_list:
-                #if hasattr(v, "__getstate__"):
-                #dc[k] = v.__getstate__()
-                #else:
                 dc[k] = v
         return dc
  
@@ -212,12 +213,6 @@ class Pickleable(object):
         self.__dict__.update(state)
         return self
 
-    #def __getstate__(self, memo):
-    #    raise NotImplementedError, "get state must be implemented to be able to pickle objects"
-
-    #def __setstate__(self, memo):
-    #    raise NotImplementedError, "set state must be implemented to be able to pickle objects"
-
 class Gradcheckable(Pickleable, Parentable):
     """
     Adds the functionality for an object to be gradcheckable.
@@ -644,6 +639,7 @@ class OptimizationHandlable(Constrainable):
         else: names = [adjust(x.name) for x in self._parameters_]
         if add_self: names = map(lambda x: adjust(self.name) + "." + x, names)
         return names
+
     def _get_param_names(self):
         n = np.array([p.hierarchy_name() + '[' + str(i) + ']' for p in self.flattened_parameters for i in p._indices()])
         return n
@@ -710,12 +706,14 @@ class Parameterizable(OptimizationHandlable):
         super(Parameterizable, self).__init__(*args, **kwargs)
         from GPy.core.parameterization.lists_and_dicts import ArrayList
         self._parameters_ = ArrayList()
+        self._param_array_ = None
         self.size = 0
         self._added_names_ = set()
+        self.__visited = False # for traversing in reverse order we need to know if we were here already
 
     @property
     def param_array(self):
-        if not hasattr(self, '_param_array_'):
+        if self._param_array_ is None:
             self._param_array_ = np.empty(self.size, dtype=np.float64)
         return self._param_array_
 
@@ -723,6 +721,42 @@ class Parameterizable(OptimizationHandlable):
     def param_array(self, arr):
         self._param_array_ = arr
 
+    def traverse(self, visit, *args, **kwargs):
+        """
+        Traverse the hierarchy performing visit(self, *args, **kwargs) at every node passed by.
+        See "visitor pattern" in literature. This is implemented in pre-order fashion.
+
+        Example:
+        Collect all children:
+
+        children = []
+        self.traverse(children.append)
+        print children
+        """
+        if not self.__visited:
+            visit(self, *args, **kwargs)
+            self.__visited = True
+            for c in self._parameters_:
+                c.traverse(visit, *args, **kwargs)
+
+    def traverse_parents(self, visit, *args, **kwargs):
+        """
+        Traverse the hierarchy upwards, visiting all parents and their children.
+        See "visitor pattern" in literature. This is implemented in pre-order fashion.
+
+        Example:
+
+        parents = []
+        self.traverse_parents(parents.append)
+        print parents
+        """
+        if not self.__visited:
+            visit(self, *args, **kwargs)
+            self.__visited = True
+            if self.has_parent():
+                self._parent_.traverse_parents(visit, *args, **kwargs)
+                self._parent_.traverse(visit, *args, **kwargs)
+            self.__visited = False
     #=========================================================================
     # Gradient handling
     #=========================================================================