merged

GPy/core/model.py

@@ -60,20 +60,6 @@ class Model(Parameterized):
         self.priors = state.pop()
         Parameterized._setstate(self, state)
 
-    def randomize(self):
-        """
-        Randomize the model.
-        Make this draw from the prior if one exists, else draw from N(0,1)
-        """
-        # first take care of all parameters (from N(0,1))
-        # x = self._get_params_transformed()
-        x = np.random.randn(self.size_transformed)
-        x = self._untransform_params(x)
-        # now draw from prior where possible
-        [np.put(x, ind, p.rvs(ind.size)) for p, ind in self.priors.iteritems() if not p is None]
-        self._set_params(x)
-        # self._set_params_transformed(self._get_params_transformed()) # makes sure all of the tied parameters get the same init (since there's only one prior object...)
-
     def optimize_restarts(self, num_restarts=10, robust=False, verbose=True, parallel=False, num_processes=None, **kwargs):
         """
         Perform random restarts of the model, and set the model to the best

GPy/core/parameterization/array_core.py

@@ -62,10 +62,11 @@ class ObservableArray(np.ndarray, Observable):
     def __setitem__(self, s, val):
         if self._s_not_empty(s):
             super(ObservableArray, self).__setitem__(s, val)
-            self._notify_observers()
+            self._notify_observers(self[s])
                 
     def __getslice__(self, start, stop):
         return self.__getitem__(slice(start, stop))
+    
     def __setslice__(self, start, stop, val):
         return self.__setitem__(slice(start, stop), val)
 

GPy/core/parameterization/lists_and_dicts.py

@@ -0,0 +1,18 @@
+'''
+Created on 27 Feb 2014
+
+@author: maxz
+'''
+
+class ParamList(list):
+    """
+    List to store ndarray-likes in.
+    It will look for 'is' instead of calling __eq__ on each element.
+    """
+    def __contains__(self, other):
+        for el in self:
+            if el is other:
+                return True
+        return False
+
+    pass

GPy/core/parameterization/param.py

@@ -3,8 +3,8 @@
 
 import itertools
 import numpy
-from parameter_core import Constrainable, Gradcheckable, Indexable, Parentable, adjust_name_for_printing
+from parameter_core import OptimizationHandlable, Gradcheckable, adjust_name_for_printing
-from array_core import ObservableArray, ParamList
+from array_core import ObservableArray
 
 ###### printing
 __constraints_name__ = "Constraint"
@@ -15,7 +15,7 @@ __precision__ = numpy.get_printoptions()['precision'] # numpy printing precision
 __print_threshold__ = 5
 ######
 
-class Param(Constrainable, ObservableArray, Gradcheckable):
+class Param(OptimizationHandlable, ObservableArray, Gradcheckable):
     """
     Parameter object for GPy models.
 
@@ -148,8 +148,11 @@ class Param(Constrainable, ObservableArray, Gradcheckable):
     #===========================================================================
     # get/set parameters
     #===========================================================================
-    def _set_params(self, param, update=True):
+    def _set_params(self, param, trigger_parent=True):
         self.flat = param
+        if trigger_parent: min_priority = None
+        else: min_priority = -numpy.inf
+        self._notify_observers(None, min_priority)
 
     def _get_params(self):
         return self.flat
@@ -172,11 +175,9 @@ class Param(Constrainable, ObservableArray, Gradcheckable):
         try: new_arr._current_slice_ = s; new_arr._original_ = self.base is new_arr.base
         except AttributeError: pass  # returning 0d array or float, double etc
         return new_arr
+    
     def __setitem__(self, s, val):
         super(Param, self).__setitem__(s, val)
-        if self.has_parent():
-            self._direct_parent_._notify_parameters_changed()
-        #self._notify_observers()
         
     #===========================================================================
     # Index Operations:
@@ -204,6 +205,7 @@ class Param(Constrainable, ObservableArray, Gradcheckable):
         ind = self._indices(slice_index)
         if ind.ndim < 2: ind = ind[:, None]
         return numpy.asarray(numpy.apply_along_axis(lambda x: numpy.sum(extended_realshape * x), 1, ind), dtype=int)
+    
     def _expand_index(self, slice_index=None):
         # this calculates the full indexing arrays from the slicing objects given by get_item for _real..._ attributes
         # it basically translates slices to their respective index arrays and turns negative indices around
@@ -345,6 +347,7 @@ class ParamConcatenation(object):
         See :py:class:`GPy.core.parameter.Param` for more details on constraining.
         """
         # self.params = params
+        from lists_and_dicts import ParamList
         self.params = ParamList([])
         for p in params:
             for p in p.flattened_parameters:

GPy/core/parameterization/parameter_core.py

@@ -2,6 +2,7 @@
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
 from transformations import Transformation, Logexp, NegativeLogexp, Logistic, __fixed__, FIXED, UNFIXED
+import heapq
 
 __updated__ = '2013-12-16'
 
@@ -11,25 +12,43 @@ def adjust_name_for_printing(name):
     return ''
 
 class Observable(object):
+    _updated = True
     def __init__(self, *args, **kwargs):
-        from collections import defaultdict
+        self._observer_callables_ = []
-        self._observer_callables_ = defaultdict(list)
-
-    def add_observer(self, observer, callble):
-        self._observer_callables_[observer].append(callble)
 
+    def add_observer(self, observer, callble, priority=0):
+        heapq.heappush(self._observer_callables_, (priority, observer, callble))
+    
     def remove_observer(self, observer, callble=None):
-        if observer in self._observer_callables_:
+        to_remove = []
-            if callble is None:
+        for p, obs, clble in self._observer_callables_:
-                del self._observer_callables_[observer]
+            if callble is not None:
-            elif callble in self._observer_callables_[observer]:
+                if (obs == observer) and (callble == clble):
-                self._observer_callables_[observer].remove(callble)
+                    to_remove.append((p, obs, clble))
-                if len(self._observer_callables_[observer]) == 0:
+            else:
-                    self.remove_observer(observer)
+                if obs is observer:
-
+                    to_remove.append((p, obs, clble))
-    def _notify_observers(self):
+        for r in to_remove:
-        [[callble(self) for callble in callables]
+            self._observer_callables_.remove(r)
-         for callables in self._observer_callables_.itervalues()]
+                
+    def _notify_observers(self, which=None, min_priority=None):
+        """
+        Notifies all observers. Which is the element, which kicked off this 
+        notification loop.
+        
+        NOTE: notifies only observers with priority p > min_priority!
+                                                    ^^^^^^^^^^^^^^^^
+        
+        :param which: object, which started this notification loop
+        :param min_priority: only notify observers with priority > min_priority
+                             if min_priority is None, notify all observers in order
+        """
+        if which is None:
+            which = self
+        if min_priority is None:
+            [callble(which) for _, _, callble in heapq.nlargest(len(self._observer_callables_), self._observer_callables_)]
+        else:
+            [callble(which) for p, _, callble in heapq.nlargest(len(self._observer_callables_), self._observer_callables_) if p > min_priority]
 
 class Pickleable(object):
     def _getstate(self):
@@ -205,9 +224,9 @@ class Constrainable(Nameable, Indexable):
     #===========================================================================
     # Prior Operations
     #===========================================================================
-    def set_prior(self, prior, warning=True, update=True):
+    def set_prior(self, prior, warning=True):
         repriorized = self.unset_priors()
-        self._add_to_index_operations(self.priors, repriorized, prior, warning, update)
+        self._add_to_index_operations(self.priors, repriorized, prior, warning)
     
     def unset_priors(self, *priors):
         return self._remove_from_index_operations(self.priors, priors)
@@ -233,7 +252,7 @@ class Constrainable(Nameable, Indexable):
     # Constrain operations -> done
     #===========================================================================
 
-    def constrain(self, transform, warning=True, update=True):
+    def constrain(self, transform, warning=True):
         """
         :param transform: the :py:class:`GPy.core.transformations.Transformation`
                           to constrain the this parameter to.
@@ -243,9 +262,9 @@ class Constrainable(Nameable, Indexable):
         :py:class:`GPy.core.transformations.Transformation`.
         """
         if isinstance(transform, Transformation):
-            self._set_params(transform.initialize(self._get_params()), update=False)
+            self._set_params(transform.initialize(self._get_params()), trigger_parent=True)
         reconstrained = self.unconstrain()
-        self._add_to_index_operations(self.constraints, reconstrained, transform, warning, update)
+        self._add_to_index_operations(self.constraints, reconstrained, transform, warning)
 
     def unconstrain(self, *transforms):
         """
@@ -256,30 +275,30 @@ class Constrainable(Nameable, Indexable):
         """
         return self._remove_from_index_operations(self.constraints, transforms)
     
-    def constrain_positive(self, warning=True, update=True):
+    def constrain_positive(self, warning=True):
         """
         :param warning: print a warning if re-constraining parameters.
 
         Constrain this parameter to the default positive constraint.
         """
-        self.constrain(Logexp(), warning=warning, update=update)
+        self.constrain(Logexp(), warning=warning)
 
-    def constrain_negative(self, warning=True, update=True):
+    def constrain_negative(self, warning=True):
         """
         :param warning: print a warning if re-constraining parameters.
 
         Constrain this parameter to the default negative constraint.
         """
-        self.constrain(NegativeLogexp(), warning=warning, update=update)
+        self.constrain(NegativeLogexp(), warning=warning)
 
-    def constrain_bounded(self, lower, upper, warning=True, update=True):
+    def constrain_bounded(self, lower, upper, warning=True):
         """
         :param lower, upper: the limits to bound this parameter to
         :param warning: print a warning if re-constraining parameters.
 
         Constrain this parameter to lie within the given range.
         """
-        self.constrain(Logistic(lower, upper), warning=warning, update=update)
+        self.constrain(Logistic(lower, upper), warning=warning)
 
     def unconstrain_positive(self):
         """
@@ -309,12 +328,10 @@ class Constrainable(Nameable, Indexable):
         for p in self._parameters_:
             p._parent_changed(parent)
 
-    def _add_to_index_operations(self, which, reconstrained, transform, warning, update):
+    def _add_to_index_operations(self, which, reconstrained, transform, warning):
         if warning and reconstrained.size > 0:
             print "WARNING: reconstraining parameters {}".format(self.parameter_names() or self.name)
         which.add(transform, self._raveled_index())
-        if update:
-            self._notify_observers()
 
     def _remove_from_index_operations(self, which, transforms):
         if len(transforms) == 0:
@@ -329,11 +346,72 @@ class Constrainable(Nameable, Indexable):
         
         return removed
 
+class OptimizationHandlable(Constrainable, Observable):
+    def _get_params_transformed(self):
+        # transformed parameters (apply transformation rules)
+        p = self._get_params()
+        [np.put(p, ind, c.finv(p[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__]
+        if self._has_fixes():
+            return p[self._fixes_]
+        return p
+    
+    def _set_params_transformed(self, p):
+        # inverse apply transformations for parameters and set the resulting parameters
+        self._set_params(self._untransform_params(p))
+    
+    def _untransform_params(self, p):
+        p = p.copy()
+        if self._has_fixes(): tmp = self._get_params(); tmp[self._fixes_] = p; p = tmp; del tmp
+        [np.put(p, ind, c.f(p[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__]
+        return p
+    
+    def _get_params(self):
+        # don't overwrite this anymore!
+        if not self.size:
+            return np.empty(shape=(0,), dtype=np.float64)
+        return np.hstack([x._get_params() for x in self._parameters_ if x.size > 0])
 
-class Parameterizable(Constrainable, Observable):
+    def _set_params(self, params, trigger_parent=True):
+        # don't overwrite this anymore!
+        raise NotImplementedError, "This needs to be implemented seperately"
+    
+    #===========================================================================
+    # Optimization handles:
+    #===========================================================================
+    def _get_param_names(self):
+        n = np.array([p.hirarchy_name() + '[' + str(i) + ']' for p in self.flattened_parameters for i in p._indices()])
+        return n
+    def _get_param_names_transformed(self):
+        n = self._get_param_names()
+        if self._has_fixes():
+            return n[self._fixes_]
+        return n
+
+    #===========================================================================
+    # Randomizeable
+    #===========================================================================
+    def randomize(self):
+        """
+        Randomize the model.
+        Make this draw from the prior if one exists, else draw from N(0,1)
+        """
+        import numpy as np
+        # first take care of all parameters (from N(0,1))
+        # x = self._get_params_transformed()
+        x = np.random.randn(self.size_transformed)
+        x = self._untransform_params(x)
+        # now draw from prior where possible
+        [np.put(x, ind, p.rvs(ind.size)) for p, ind in self.priors.iteritems() if not p is None]
+        self._set_params(x)
+        # self._set_params_transformed(self._get_params_transformed()) # makes sure all of the tied parameters get the same init (since there's only one prior object...)
+    
+
+import numpy as np
+
+class Parameterizable(OptimizationHandlable):
     def __init__(self, *args, **kwargs):
         super(Parameterizable, self).__init__(*args, **kwargs)
-        from GPy.core.parameterization.array_core import ParamList
+        from GPy.core.parameterization.lists_and_dicts import ParamList
         _parameters_ = ParamList()
         self._added_names_ = set()
     
@@ -377,28 +455,26 @@ class Parameterizable(Constrainable, Observable):
         import itertools
         [p._collect_gradient(target[s]) for p, s in itertools.izip(self._parameters_, self._param_slices_)]
 
+    def _set_params(self, params, trigger_parent=True):
+        import itertools
+        [p._set_params(params[s], trigger_parent=False) for p, s in itertools.izip(self._parameters_, self._param_slices_)]
+        if trigger_parent: min_priority = None
+        else: min_priority = -np.inf
+        self._notify_observers(None, min_priority)
+
     def _set_gradient(self, g):
         import itertools
         [p._set_gradient(g[s]) for p, s in itertools.izip(self._parameters_, self._param_slices_)]
 
-    def _get_params(self):
+    
-        import numpy as np
+    #===========================================================================
-        # don't overwrite this anymore!
+    # TODO: not working yet
-        if not self.size:
+    #===========================================================================
-            return np.empty(shape=(0,), dtype=np.float64)
-        return np.hstack([x._get_params() for x in self._parameters_ if x.size > 0])
-
-    def _set_params(self, params, update=True):
-        # don't overwrite this anymore!
-        import itertools
-        [p._set_params(params[s]) for p, s in itertools.izip(self._parameters_, self._param_slices_)]
-        self._notify_parameters_changed()
-
     def copy(self):
         """Returns a (deep) copy of the current model"""
         import copy
         from .index_operations import ParameterIndexOperations, ParameterIndexOperationsView
-        from .array_core import ParamList
+        from .lists_and_dicts import ParamList
 
         dc = dict()
         for k, v in self.__dict__.iteritems():
@@ -424,12 +500,6 @@ class Parameterizable(Constrainable, Observable):
             s.add_parameter(p)
         
         return s
-
-    def _notify_parameters_changed(self):
-        self.parameters_changed()
-        self._notify_observers()
-        if self.has_parent():
-            self._direct_parent_._notify_parameters_changed()
         
     def parameters_changed(self):
         """

GPy/core/parameterization/parameterized.py

@@ -58,6 +58,7 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable):
         self._in_init_ = True
         self._parameters_ = ParamList()
         self.size = sum(p.size for p in self._parameters_)
+        self.add_observer(self, self._parameters_changed_notification, -100)
         if not self._has_fixes():
             self._fixes_ = None
         self._param_slices_ = []
@@ -65,7 +66,7 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable):
         del self._in_init_
 
     def build_pydot(self, G=None):
-        import pydot
+        import pydot  # @UnresolvedImport
         iamroot = False
         if G is None:
             G = pydot.Dot(graph_type='digraph')
@@ -116,6 +117,7 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable):
                 self.constraints.update(param.constraints, start)
                 self.priors.update(param.priors, start)
                 self._parameters_.insert(index, param)
+            param.add_observer(self, self._pass_through_notify_observers, -np.inf)
             self.size += param.size
         else:
             raise RuntimeError, """Parameter exists already added and no copy made"""
@@ -170,6 +172,13 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable):
             self._add_parameter_name(p)
 
     #===========================================================================
+    # notification system
+    #===========================================================================
+    def _parameters_changed_notification(self, which):
+        self.parameters_changed()
+    def _pass_through_notify_observers(self, which):
+        self._notify_observers(which)
+    #===========================================================================
     # Pickling operations
     #===========================================================================
     def pickle(self, f, protocol=-1):
@@ -237,32 +246,7 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable):
                 g[self._offset_for(p) + numpy.array(list(i))] += g[self._raveled_index_for(t)]
         if self._has_fixes(): return g[self._fixes_]
         return g
-    #===========================================================================
+
-    # Optimization handles:
-    #===========================================================================
-    def _get_param_names(self):
-        n = numpy.array([p.hirarchy_name() + '[' + str(i) + ']' for p in self.flattened_parameters for i in p._indices()])
-        return n
-    def _get_param_names_transformed(self):
-        n = self._get_param_names()
-        if self._has_fixes():
-            return n[self._fixes_]
-        return n
-    def _get_params_transformed(self):
-        # transformed parameters (apply transformation rules)
-        p = self._get_params()
-        [numpy.put(p, ind, c.finv(p[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__]
-        if self._has_fixes():
-            return p[self._fixes_]
-        return p
-    def _set_params_transformed(self, p):
-        # inverse apply transformations for parameters and set the resulting parameters
-        self._set_params(self._untransform_params(p))
-    def _untransform_params(self, p):
-        p = p.copy()
-        if self._has_fixes(): tmp = self._get_params(); tmp[self._fixes_] = p; p = tmp; del tmp
-        [numpy.put(p, ind, c.f(p[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__]
-        return p
     #===========================================================================
     # Indexable Handling
     #===========================================================================
@@ -297,6 +281,7 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable):
         this is not in the global view of things!
         """
         return numpy.r_[:self.size]
+    
     #===========================================================================
     # Fixing parameters:
     #===========================================================================
@@ -304,6 +289,7 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable):
         if self._has_fixes():
             return self._fixes_[self._raveled_index_for(param)]
         return numpy.ones(self.size, dtype=bool)[self._raveled_index_for(param)]
+    
     #===========================================================================
     # Convenience for fixed, tied checking of param:
     #===========================================================================

GPy/core/parameterization/priors.py

@@ -64,6 +64,36 @@ class Gaussian(Prior):
         return np.random.randn(n) * self.sigma + self.mu
 
 
+class Uniform(Prior):
+    domain = _REAL
+    _instances = []
+    def __new__(cls, lower, upper): # Singleton:
+        if cls._instances:
+            cls._instances[:] = [instance for instance in cls._instances if instance()]
+            for instance in cls._instances:
+                if instance().lower == lower and instance().upper == upper:
+                    return instance()
+        o = super(Prior, cls).__new__(cls, lower, upper)
+        cls._instances.append(weakref.ref(o))
+        return cls._instances[-1]()
+    
+    def __init__(self, lower, upper):
+        self.lower = float(lower)
+        self.upper = float(upper)
+    
+    def __str__(self):
+        return "[" + str(np.round(self.lower)) + ', ' + str(np.round(self.upper)) + ']'
+
+    def lnpdf(self, x):
+        region = (x>=self.lower) * (x<=self.upper)
+        return region
+
+    def lnpdf_grad(self, x):
+        return np.zeros(x.shape)
+
+    def rvs(self, n):
+        return np.random.uniform(self.lower, self.upper, size=n)
+    
 class LogGaussian(Prior):
     """
     Implementation of the univariate *log*-Gaussian probability function, coupled with random variables.

GPy/core/parameterization/transformations.py

@@ -6,8 +6,11 @@ import numpy as np
 from domains import _POSITIVE,_NEGATIVE, _BOUNDED
 import weakref
 
+import sys
+#_lim_val = -np.log(sys.float_info.epsilon)
+
 _exp_lim_val = np.finfo(np.float64).max
-_lim_val = np.log(_exp_lim_val)#-np.log(sys.float_info.epsilon)
+_lim_val = np.log(_exp_lim_val)#
 
 #===============================================================================
 # Fixing constants
@@ -35,7 +38,6 @@ class Transformation(object):
         """ produce a sensible initial value for f(x)"""
         raise NotImplementedError
     def plot(self, xlabel=r'transformed $\theta$', ylabel=r'$\theta$', axes=None, *args,**kw):
-        import sys
         assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
         import matplotlib.pyplot as plt
         from ...plotting.matplot_dep import base_plots
@@ -52,7 +54,7 @@ class Transformation(object):
 class Logexp(Transformation):
     domain = _POSITIVE
     def f(self, x):
-        return np.where(x>_lim_val, x, np.log(1. + np.exp(np.clip(x, -np.inf, _lim_val))))
+        return np.where(x>_lim_val, x, np.log(1. + np.exp(np.clip(x, -_lim_val, _lim_val))))
         #raises overflow warning: return np.where(x>_lim_val, x, np.log(1. + np.exp(x)))
     def finv(self, f):
         return np.where(f>_lim_val, f, np.log(np.exp(f) - 1.))

GPy/inference/latent_function_inference/var_dtc.py

@@ -60,7 +60,7 @@ class VarDTC(object):
         _, output_dim = Y.shape
 
         #see whether we've got a different noise variance for each datum
-        beta = 1./np.squeeze(likelihood.variance)
+        beta = 1./max(1e-6, np.squeeze(likelihood.variance))
 
         # VVT_factor is a matrix such that tdot(VVT_factor) = VVT...this is for efficiency!
         #self.YYTfactor = self.get_YYTfactor(Y)
@@ -214,7 +214,7 @@ class VarDTCMissingData(object):
             psi2_all = None
 
         Ys, traces = self._Y(Y)
-        beta_all = 1./likelihood.variance
+        beta_all = 1./max(1e-6, likelihood.variance)
         het_noise = beta_all.size != 1
 
         import itertools

GPy/kern/_src/rbf.py

@@ -20,7 +20,7 @@ class RBF(Stationary):
 
     """
 
-    def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, name='RBF'):
+    def __init__(self, input_dim, variance=1., lengthscale=None, ARD=False, name='rbf'):
         super(RBF, self).__init__(input_dim, variance, lengthscale, ARD, name)
         self.weave_options = {}
 
@@ -159,7 +159,7 @@ class RBF(Stationary):
         grad_mu = np.sum(dL_dpsi1[:, :, None] * tmp * dist, 1)
         grad_S = np.sum(dL_dpsi1[:, :, None] * 0.5 * tmp * (dist_sq - 1), 1)
         #psi2
-        denom, Zdist, Zdist_sq, mudist, mudist_sq, psi2 = self._psi2computations(Z, variational_posterior)
+        denom, _, _, mudist, mudist_sq, psi2 = self._psi2computations(Z, variational_posterior)
         tmp = psi2[:, :, :, None] / l2 / denom
         grad_mu += -2.*(dL_dpsi2[:, :, :, None] * tmp * mudist).sum(1).sum(1)
         grad_S += (dL_dpsi2[:, :, :, None] * tmp * (2.*mudist_sq - 1)).sum(1).sum(1)
@@ -237,7 +237,7 @@ class RBF(Stationary):
         return denom, dist, dist_sq, psi1
 
 
-    #@cache_this(ignore_args=(1,))
+    @Cache_this(limit=1, ignore_args=(0,))
     def _Z_distances(self, Z):
         Zhat = 0.5 * (Z[:, None, :] + Z[None, :, :]) # M,M,Q
         Zdist = 0.5 * (Z[:, None, :] - Z[None, :, :]) # M,M,Q
@@ -257,7 +257,6 @@ class RBF(Stationary):
         #allocate memory for the things we want to compute
         mudist = np.empty((N, M, M, Q))
         mudist_sq = np.empty((N, M, M, Q))
-        exponent = np.zeros((N,M,M))
         psi2 = np.empty((N, M, M))
 
         l2 = self.lengthscale **2

GPy/kern/_src/stationary.py

@@ -69,12 +69,12 @@ class Stationary(Kern):
     def dK_dr(self, r):
         raise NotImplementedError, "implement derivative of the covariance function wrt r to use this class"
 
-    #@Cache_this(limit=5, ignore_args=())
+    @Cache_this(limit=5, ignore_args=())
     def K(self, X, X2=None):
         r = self._scaled_dist(X, X2)
         return self.K_of_r(r)
 
-    #@Cache_this(limit=5, ignore_args=(0,))
+    @Cache_this(limit=5, ignore_args=(0,))
     def _unscaled_dist(self, X, X2=None):
         """
         Compute the Euclidean distance between each row of X and X2, or between
@@ -88,7 +88,7 @@ class Stationary(Kern):
             X2sq = np.sum(np.square(X2),1)
             return np.sqrt(-2.*np.dot(X, X2.T) + (X1sq[:,None] + X2sq[None,:]))
 
-    #@Cache_this(limit=5, ignore_args=())
+    @Cache_this(limit=5, ignore_args=())
     def _scaled_dist(self, X, X2=None):
         """
         Efficiently compute the scaled distance, r.
@@ -141,7 +141,7 @@ class Stationary(Kern):
         diagonal, where we return zero (the distance on the diagonal is zero).
         This term appears in derviatives.
         """
-        dist = self._scaled_dist(X, X2)
+        dist = self._scaled_dist(X, X2).copy()
         if X2 is None:
             nondiag = util.diag.offdiag_view(dist)
             nondiag[:] = 1./nondiag

GPy/plotting/matplot_dep/models_plots.py

@@ -56,10 +56,13 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
     if ax is None:
         fig = pb.figure(num=fignum)
         ax = fig.add_subplot(111)
-
+    
-    X, Y = param_to_array(model.X, model.Y)
+    if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs(): 
-    if hasattr(model, 'has_uncertain_inputs') and model.has_uncertain_inputs(): X_variance = model.X_variance
+        X = model.X.mean
-
+        X_variance = param_to_array(model.X.variance)
+    else:
+        X = model.X
+    X, Y = param_to_array(X, model.Y)
     if hasattr(model, 'Z'): Z = param_to_array(model.Z)
 
     #work out what the inputs are for plotting (1D or 2D)
@@ -98,10 +101,10 @@ def plot_fit(model, plot_limits=None, which_data_rows='all',
 
 
         #add error bars for uncertain (if input uncertainty is being modelled)
-        #if hasattr(model,"has_uncertain_inputs") and model.has_uncertain_inputs():
+        if hasattr(model,"has_uncertain_inputs") and model.has_uncertain_inputs():
-        #    ax.errorbar(X[which_data_rows, free_dims].flatten(), Y[which_data_rows, which_data_ycols].flatten(),
+            ax.errorbar(X[which_data_rows, free_dims].flatten(), Y[which_data_rows, which_data_ycols].flatten(),
-        #                xerr=2 * np.sqrt(X_variance[which_data_rows, free_dims].flatten()),
+                        xerr=2 * np.sqrt(X_variance[which_data_rows, free_dims].flatten()),
-        #                ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
+                        ecolor='k', fmt=None, elinewidth=.5, alpha=.5)
 
 
         #set the limits of the plot to some sensible values

GPy/testing/observable_tests.py

@@ -0,0 +1,133 @@
+'''
+Created on 27 Feb 2014
+
+@author: maxz
+'''
+import unittest
+from GPy.core.parameterization.parameterized import Parameterized
+from GPy.core.parameterization.param import Param
+import numpy
+
+
+class ParamTestParent(Parameterized):
+    parent_changed_count = 0
+    def parameters_changed(self):
+        self.parent_changed_count += 1
+
+class ParameterizedTest(Parameterized):
+    params_changed_count = 0
+    def parameters_changed(self):
+        self.params_changed_count += 1
+    def _set_params(self, params, trigger_parent=True):
+        Parameterized._set_params(self, params, trigger_parent=trigger_parent)
+
+class Test(unittest.TestCase):
+    
+    def setUp(self):
+        self.parent = ParamTestParent('test parent')
+        self.par = ParameterizedTest('test model')
+        self.par2 = ParameterizedTest('test model 2')
+        self.p = Param('test parameter', numpy.random.normal(1,2,(10,3)))
+
+        self.par.add_parameter(self.p)
+        self.par.add_parameter(Param('test1', numpy.random.normal(0,1,(1,))))
+        self.par.add_parameter(Param('test2', numpy.random.normal(0,1,(1,))))
+
+        self.par2.add_parameter(Param('par2 test1', numpy.random.normal(0,1,(1,))))
+        self.par2.add_parameter(Param('par2 test2', numpy.random.normal(0,1,(1,))))
+
+        self.parent.add_parameter(self.par)
+        self.parent.add_parameter(self.par2)
+        
+        self._observer_triggered = None
+        self._trigger_count = 0
+        self._first = None
+        self._second = None
+        
+    def _trigger(self, which):
+        self._observer_triggered = float(which)
+        self._trigger_count += 1
+        if self._first is not None:
+            self._second = self._trigger
+        else:
+            self._first = self._trigger
+    
+    def _trigger_priority(self, which):
+        if self._first is not None:
+            self._second = self._trigger_priority
+        else:
+            self._first = self._trigger_priority
+            
+    def test_observable(self):
+        self.par.add_observer(self, self._trigger, -1)
+        self.assertEqual(self.par.params_changed_count, 0, 'no params changed yet')
+        self.assertEqual(self.par.params_changed_count, self.parent.parent_changed_count, 'parent should be triggered as often as param')
+
+        self.p[0,1] = 3 # trigger observers
+        self.assertEqual(self._observer_triggered, 3, 'observer should have triggered')
+        self.assertEqual(self._trigger_count, 1, 'observer should have triggered once')
+        self.assertEqual(self.par.params_changed_count, 1, 'params changed once')
+        self.assertEqual(self.par.params_changed_count, self.parent.parent_changed_count, 'parent should be triggered as often as param')
+
+        self.par.remove_observer(self)
+        self.p[2,1] = 4
+        self.assertEqual(self._observer_triggered, 3, 'observer should not have triggered')
+        self.assertEqual(self._trigger_count, 1, 'observer should have triggered once')
+        self.assertEqual(self.par.params_changed_count, 2, 'params changed second')
+        self.assertEqual(self.par.params_changed_count, self.parent.parent_changed_count, 'parent should be triggered as often as param')
+        
+        self.par.add_observer(self, self._trigger, -1)
+        self.p[2,1] = 4
+        self.assertEqual(self._observer_triggered, 4, 'observer should have triggered')
+        self.assertEqual(self._trigger_count, 2, 'observer should have triggered once')
+        self.assertEqual(self.par.params_changed_count, 3, 'params changed second')
+        self.assertEqual(self.par.params_changed_count, self.parent.parent_changed_count, 'parent should be triggered as often as param')
+        
+        self.par.remove_observer(self, self._trigger)
+        self.p[0,1] = 3
+        self.assertEqual(self._observer_triggered, 4, 'observer should not have triggered')
+        self.assertEqual(self._trigger_count, 2, 'observer should have triggered once')
+        self.assertEqual(self.par.params_changed_count, 4, 'params changed second')
+        self.assertEqual(self.par.params_changed_count, self.parent.parent_changed_count, 'parent should be triggered as often as param')
+
+    def test_set_params(self):
+        self.assertEqual(self.par.params_changed_count, 0, 'no params changed yet')
+        self.par._set_params(numpy.ones(self.par.size))
+        self.assertEqual(self.par.params_changed_count, 1, 'now params changed')
+        self.assertEqual(self.parent.parent_changed_count, self.par.params_changed_count)
+        
+        self.parent._set_params(numpy.ones(self.parent.size) * 2)
+        self.assertEqual(self.par.params_changed_count, 2, 'now params changed')
+        self.assertEqual(self.parent.parent_changed_count, self.par.params_changed_count)
+
+
+    def test_priority_notify(self):
+        self.assertEqual(self.par.params_changed_count, 0)
+        self.par._notify_observers(0, None)
+        self.assertEqual(self.par.params_changed_count, 1)
+        self.assertEqual(self.parent.parent_changed_count, self.par.params_changed_count)
+
+        self.par._notify_observers(0, -numpy.inf)
+        self.assertEqual(self.par.params_changed_count, 2)
+        self.assertEqual(self.parent.parent_changed_count, 1)
+
+    def test_priority(self):
+        self.par.add_observer(self, self._trigger, -1)
+        self.par.add_observer(self, self._trigger_priority, 0)
+        self.par._notify_observers(0)
+        self.assertEqual(self._first, self._trigger_priority, 'priority should be first')
+        self.assertEqual(self._second, self._trigger, 'priority should be first')
+
+        self.par.remove_observer(self)
+        self._first = self._second = None
+        
+        self.par.add_observer(self, self._trigger, 1)
+        self.par.add_observer(self, self._trigger_priority, 0)
+        self.par._notify_observers(0)
+        self.assertEqual(self._first, self._trigger, 'priority should be second')
+        self.assertEqual(self._second, self._trigger_priority, 'priority should be second')
+        
+
+if __name__ == "__main__":
+    #import sys;sys.argv = ['', 'Test.testName']
+    unittest.main()

GPy/util/caching.py

@@ -38,6 +38,9 @@ class Cacher(object):
         if not all([isinstance(arg, Observable) for arg in observable_args]):
             return self.operation(*args)
 
+        # TODO: WARNING !!! Cache OFFSWITCH !!! WARNING
+        # return self.operation(*args)
+        
         #if the result is cached, return the cached computation
         state = [all(a is b for a, b in zip(args, cached_i)) for cached_i in self.cached_inputs]
         if any(state):