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

GPy/core/parameterization/parameter_core.py

@@ -16,7 +16,7 @@ Observable Pattern for patameterization
 from transformations import Transformation, Logexp, NegativeLogexp, Logistic, __fixed__, FIXED, UNFIXED
 import numpy as np
 
-__updated__ = '2014-03-11'
+__updated__ = '2014-03-12'
 
 class HierarchyError(Exception):
     """
@@ -796,27 +796,27 @@ class Parameterizable(OptimizationHandlable):
         """
         if not param in self._parameters_:
             raise RuntimeError, "Parameter {} does not belong to this object, remove parameters directly from their respective parents".format(param._short())
-        
+
         start = sum([p.size for p in self._parameters_[:param._parent_index_]])
         self._remove_parameter_name(param)
         self.size -= param.size
         del self._parameters_[param._parent_index_]
-        
+
         param._disconnect_parent()
         param.remove_observer(self, self._pass_through_notify_observers)
         self.constraints.shift_left(start, param.size)
-        
+
         self._connect_fixes()
         self._connect_parameters()
         self._notify_parent_change()
-        
+
         parent = self._parent_
         while parent is not None:
             parent._connect_fixes()
             parent._connect_parameters()
             parent._notify_parent_change()
             parent = parent._parent_
-        
+
     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
@@ -829,29 +829,26 @@ class Parameterizable(OptimizationHandlable):
         old_size = 0
         self._param_array_ = np.empty(self.size, dtype=np.float64)
         self._gradient_array_ = np.empty(self.size, dtype=np.float64)
-        
+
         self._param_slices_ = []
-        
         for i, p in enumerate(self._parameters_):
             p._parent_ = self
             p._parent_index_ = i
-            
+
             pslice = slice(old_size, old_size+p.size)
-            
             # first connect all children
             p._propagate_param_grad(self._param_array_[pslice], self._gradient_array_[pslice])            
-            
             # then connect children to self
             self._param_array_[pslice] = p._param_array_.ravel()#, requirements=['C', 'W']).ravel(order='C')
             self._gradient_array_[pslice] = p._gradient_array_.ravel()#, requirements=['C', 'W']).ravel(order='C')
-            
+
             if not p._param_array_.flags['C_CONTIGUOUS']:
                 import ipdb;ipdb.set_trace()
             p._param_array_.data = self._param_array_[pslice].data
             p._gradient_array_.data = self._gradient_array_[pslice].data
-            
+
             self._param_slices_.append(pslice)
-            
+
             self._add_parameter_name(p, ignore_added_names=ignore_added_names)
             old_size += p.size
 
@@ -862,12 +859,13 @@ class Parameterizable(OptimizationHandlable):
         self.parameters_changed()
     def _pass_through_notify_observers(self, which):
         self.notify_observers(which)
-    
+
     #===========================================================================
     # TODO: not working yet
     #===========================================================================
     def copy(self):
         """Returns a (deep) copy of the current model"""
+        raise NotImplementedError, "Copy is not yet implemented, TODO: Observable hierarchy"
         import copy
         from .index_operations import ParameterIndexOperations, ParameterIndexOperationsView
         from .lists_and_dicts import ArrayList

GPy/inference/latent_function_inference/dtc.py

@@ -19,7 +19,7 @@ class DTC(object):
     def __init__(self):
         self.const_jitter = 1e-6
 
-    def inference(self, kern, X, X_variance, Z, likelihood, Y):
+    def inference(self, kern, X, Z, likelihood, Y):
         assert X_variance is None, "cannot use X_variance with DTC. Try varDTC."
 
         #TODO: MAX! fix this!
@@ -80,10 +80,6 @@ class DTC(object):
 
         grad_dict = {'dL_dKmm': dL_dK, 'dL_dKdiag':np.zeros_like(Knn), 'dL_dKnm':dL_dU.T}
 
-        #update gradients
-        kern.update_gradients_sparse(X=X, Z=Z, **grad_dict)
-        likelihood.update_gradients(dL_dR)
-
         #construct a posterior object
         post = Posterior(woodbury_inv=Kmmi-P, woodbury_vector=v, K=Kmm, mean=None, cov=None, K_chol=L)
 

GPy/inference/latent_function_inference/fitc.py

@@ -17,8 +17,7 @@ class FITC(object):
     def __init__(self):
         self.const_jitter = 1e-6
 
-    def inference(self, kern, X, X_variance, Z, likelihood, Y):
-        assert X_variance is None, "cannot use X_variance with FITC. Try varDTC."
+    def inference(self, kern, X, Z, likelihood, Y):
         
         #TODO: MAX! fix this!
         from ...util.misc import param_to_array
@@ -81,11 +80,7 @@ class FITC(object):
         dL_dU *= beta_star
         dL_dU -= 2.*KiU*dL_dR
 
-        grad_dict = {'dL_dKmm': dL_dK, 'dL_dKdiag':dL_dR, 'dL_dKnm':dL_dU.T}
-
-        #update gradients
-        kern.update_gradients_sparse(X=X, Z=Z, **grad_dict)
-        likelihood.update_gradients(dL_dR)
+        grad_dict = {'dL_dKmm': dL_dK, 'dL_dKdiag':dL_dR, 'dL_dKnm':dL_dU.T, 'partial_for_likelihood':dL_dR}
 
         #construct a posterior object
         post = Posterior(woodbury_inv=Kmmi-P, woodbury_vector=v, K=Kmm, mean=None, cov=None, K_chol=L)