[link|unlink_parameter] renaming add_parameter to link_parameter

GPy/core/parameterization/parameterized.py

@@ -82,7 +82,7 @@ class Parameterized(Parameterizable):
             self._fixes_ = None
         self._param_slices_ = []
         #self._connect_parameters()
-        self.add_parameters(*parameters)
+        self.link_parameters(*parameters)
 
     def build_pydot(self, G=None):
         import pydot  # @UnresolvedImport
@@ -110,7 +110,7 @@ class Parameterized(Parameterizable):
     #===========================================================================
     # Add remove parameters:
     #===========================================================================
-    def add_parameter(self, param, index=None, _ignore_added_names=False):
+    def link_parameter(self, param, index=None, _ignore_added_names=False):
         """
         :param parameters:  the parameters to add
         :type parameters:   list of or one :py:class:`GPy.core.param.Param`
@@ -122,8 +122,8 @@ class Parameterized(Parameterizable):
         at any given index using the :func:`list.insert` syntax
         """
         if param in self.parameters and index is not None:
-            self.remove_parameter(param)
-            self.add_parameter(param, index)
+            self.unlink_parameter(param)
+            self.link_parameter(param, index)
         # elif param.has_parent():
         #    raise HierarchyError, "parameter {} already in another model ({}), create new object (or copy) for adding".format(param._short(), param._highest_parent_._short())
         elif param not in self.parameters:
@@ -132,7 +132,7 @@ class Parameterized(Parameterizable):
                     if parent is self:
                         raise HierarchyError, "You cannot add a parameter twice into the hierarchy"
                 param.traverse_parents(visit, self)
-                param._parent_.remove_parameter(param)
+                param._parent_.unlink_parameter(param)
             # make sure the size is set
             if index is None:
                 start = sum(p.size for p in self.parameters)
@@ -168,14 +168,14 @@ class Parameterized(Parameterizable):
             raise HierarchyError, """Parameter exists already, try making a copy"""
 
 
-    def add_parameters(self, *parameters):
+    def link_parameters(self, *parameters):
         """
         convenience method for adding several
         parameters without gradient specification
         """
-        [self.add_parameter(p) for p in parameters]
+        [self.link_parameter(p) for p in parameters]
 
-    def remove_parameter(self, param):
+    def unlink_parameter(self, param):
         """
         :param param: param object to remove from being a parameter of this parameterized object.
         """
@@ -206,6 +206,11 @@ class Parameterized(Parameterizable):
         self._highest_parent_._connect_fixes()
         self._highest_parent_._notify_parent_change()
 
+    def add_parameter(self, *args, **kwargs):
+        raise DeprecationWarning, "add_parameter was renamed to link_parameter to avoid confusion of setting variables"
+    def remove_parameter(self, *args, **kwargs):
+        raise DeprecationWarning, "remove_parameter was renamed to link_parameter to avoid confusion of setting variables"
+
     def _connect_parameters(self, ignore_added_names=False):
         # connect parameterlist to this parameterized object
         # This just sets up the right connection for the params objects
@@ -294,7 +299,9 @@ class Parameterized(Parameterizable):
         if hasattr(self, "parameters"):
             try:
                 pnames = self.parameter_names(False, adjust_for_printing=True, recursive=False)
-                if name in pnames: self.parameters[pnames.index(name)][:] = val; return
+                if name in pnames:
+                    param = self.parameters[pnames.index(name)]
+                    param[:] = val; return
             except AttributeError:
                 pass
         object.__setattr__(self, name, val);

GPy/core/parameterization/variational.py

@@ -42,7 +42,7 @@ class SpikeAndSlabPrior(VariationalPrior):
             self.pi = Param('Pi', pi, Logistic(1e-10,1.-1e-10))
         else:
             self.pi = Param('Pi', pi, __fixed__)
-        self.add_parameter(self.pi)
+        self.link_parameter(self.pi)
 
 
     def KL_divergence(self, variational_posterior):
@@ -89,7 +89,7 @@ class VariationalPosterior(Parameterized):
         self.ndim = self.mean.ndim
         self.shape = self.mean.shape
         self.num_data, self.input_dim = self.mean.shape
-        self.add_parameters(self.mean, self.variance)
+        self.link_parameters(self.mean, self.variance)
         self.num_data, self.input_dim = self.mean.shape
         if self.has_uncertain_inputs():
             assert self.variance.shape == self.mean.shape, "need one variance per sample and dimenion"
@@ -156,7 +156,7 @@ class SpikeAndSlabPosterior(VariationalPosterior):
         """
         super(SpikeAndSlabPosterior, self).__init__(means, variances, name)
         self.gamma = Param("binary_prob",binary_prob, Logistic(1e-10,1.-1e-10))
-        self.add_parameter(self.gamma)
+        self.link_parameter(self.gamma)
 
     def __getitem__(self, s):
         if isinstance(s, (int, slice, tuple, list, np.ndarray)):