hierarchy edits. adding removing parameters withing hierarchy

GPy/core/model.py

@@ -265,7 +265,7 @@ class Model(Parameterized):
            and numerical gradients is within <tolerance> of unity.
         """
 
-        x = self._get_params_transformed().copy()
+        x = self._get_params_transformed()
 
         if not verbose:
             # make sure only to test the selected parameters
@@ -283,7 +283,7 @@ class Model(Parameterized):
                     return
 
             # just check the global ratio
-            dx = np.zeros_like(x)
+            dx = np.zeros(x.shape())
             dx[transformed_index] = step * np.sign(np.random.uniform(-1, 1, transformed_index.size))
 
             # evaulate around the point x

GPy/core/parameterization/index_operations.py

@@ -194,9 +194,13 @@ class ParameterIndexOperationsView(object):
 
 
     def shift_right(self, start, size):
-        raise NotImplementedError, 'Shifting only supported in original ParamIndexOperations'
+        self._param_index_ops.shift_right(start+self._offset, size)
-    
 
+    def shift_left(self, start, size):
+        self._param_index_ops.shift_left(start+self._offset, size)
+        self._offset -= size
+        self._size -= size
+            
     def clear(self):
         for i, ind in self.items():
             self._param_index_ops.remove(i, ind+self._offset)
@@ -232,9 +236,7 @@ class ParameterIndexOperationsView(object):
 
     def __getitem__(self, prop):
         ind = self._filter_index(self._param_index_ops[prop])
-        if ind.size > 0:
+        return ind
-            return ind
-        raise KeyError, prop
     
     def __str__(self, *args, **kwargs):
         import pprint

GPy/core/parameterization/param.py

@@ -269,7 +269,7 @@ class Param(OptimizationHandlable, ObservableArray, Gradcheckable):
         return [t._short() for t in self._tied_to_] or ['']
     def __repr__(self, *args, **kwargs):
         name = "\033[1m{x:s}\033[0;0m:\n".format(
-                            x=self.hirarchy_name())
+                            x=self.hierarchy_name())
         return name + super(Param, self).__repr__(*args, **kwargs)
     def _ties_for(self, rav_index):
         # size = sum(p.size for p in self._tied_to_)
@@ -303,12 +303,12 @@ class Param(OptimizationHandlable, ObservableArray, Gradcheckable):
         gen = map(lambda x: " ".join(map(str, x)), gen)
         return reduce(lambda a, b:max(a, len(b)), gen, len(header))
     def _max_len_values(self):
-        return reduce(lambda a, b:max(a, len("{x:=.{0}g}".format(__precision__, x=b))), self.flat, len(self.hirarchy_name()))
+        return reduce(lambda a, b:max(a, len("{x:=.{0}g}".format(__precision__, x=b))), self.flat, len(self.hierarchy_name()))
     def _max_len_index(self, ind):
         return reduce(lambda a, b:max(a, len(str(b))), ind, len(__index_name__))
     def _short(self):
         # short string to print
-        name = self.hirarchy_name()
+        name = self.hierarchy_name()
         if self._realsize_ < 2:
             return name
         ind = self._indices()
@@ -331,8 +331,8 @@ class Param(OptimizationHandlable, ObservableArray, Gradcheckable):
         if lp is None: lp = self._max_len_names(prirs, __tie_name__)
         sep = '-'
         header_format = "  {i:{5}^{2}s}  |  \033[1m{x:{5}^{1}s}\033[0;0m  |  {c:{5}^{0}s}  |  {p:{5}^{4}s}  |  {t:{5}^{3}s}"
-        if only_name: header = header_format.format(lc, lx, li, lt, lp, ' ', x=self.hirarchy_name(), c=sep*lc, i=sep*li, t=sep*lt, p=sep*lp)  # nice header for printing
+        if only_name: header = header_format.format(lc, lx, li, lt, lp, ' ', x=self.hierarchy_name(), c=sep*lc, i=sep*li, t=sep*lt, p=sep*lp)  # nice header for printing
-        else: header = header_format.format(lc, lx, li, lt, lp, ' ', x=self.hirarchy_name(), c=__constraints_name__, i=__index_name__, t=__tie_name__, p=__priors_name__)  # nice header for printing
+        else: header = header_format.format(lc, lx, li, lt, lp, ' ', x=self.hierarchy_name(), c=__constraints_name__, i=__index_name__, t=__tie_name__, p=__priors_name__)  # nice header for printing
         if not ties: ties = itertools.cycle([''])
         return "\n".join([header] + ["  {i!s:^{3}s}  |  {x: >{1}.{2}g}  |  {c:^{0}s}  |  {p:^{5}s}  |  {t:^{4}s}  ".format(lc, lx, __precision__, li, lt, lp, x=x, c=" ".join(map(str, c)), p=" ".join(map(str, p)), t=(t or ''), i=i) for i, x, c, t, p in itertools.izip(indices, vals, constr_matrix, ties, prirs)])  # return all the constraints with right indices
         # except: return super(Param, self).__str__()
@@ -356,6 +356,21 @@ class ParamConcatenation(object):
         self._param_sizes = [p.size for p in self.params]
         startstops = numpy.cumsum([0] + self._param_sizes)
         self._param_slices_ = [slice(start, stop) for start,stop in zip(startstops, startstops[1:])]
+        
+        parents = dict()
+        for p in self.params:
+            if p.has_parent():
+                parent = p._direct_parent_
+                level = 0
+                while parent is not None:
+                    if parent in parents:
+                        parents[parent] = max(level, parents[parent])
+                    else:
+                        parents[parent] = level
+                    level += 1
+                    parent = parent._direct_parent_
+        import operator
+        self.parents = map(lambda x: x[0], sorted(parents.iteritems(), key=operator.itemgetter(1)))
     #===========================================================================
     # Get/set items, enable broadcasting
     #===========================================================================
@@ -369,24 +384,26 @@ class ParamConcatenation(object):
             val = val._vals()
         ind = numpy.zeros(sum(self._param_sizes), dtype=bool); ind[s] = True;
         vals = self._vals(); vals[s] = val; del val
-        [numpy.place(p, ind[ps], vals[ps]) and update and p._notify_observers()
+        [numpy.place(p, ind[ps], vals[ps])
          for p, ps in zip(self.params, self._param_slices_)]
+        if update:
+            self.update_all_params()
     def _vals(self):
         return numpy.hstack([p._get_params() for p in self.params])
     #===========================================================================
     # parameter operations:
     #===========================================================================
     def update_all_params(self):
-        for p in self.params:
+        for par in self.parents:
-            p._notify_observers()
+            par._notify_observers(-numpy.inf)
-
+        
     def constrain(self, constraint, warning=True):
-        [param.constrain(constraint, update=False) for param in self.params]
+        [param.constrain(constraint, trigger_parent=False) for param in self.params]
         self.update_all_params()
     constrain.__doc__ = Param.constrain.__doc__
 
     def constrain_positive(self, warning=True):
-        [param.constrain_positive(warning, update=False) for param in self.params]
+        [param.constrain_positive(warning, trigger_parent=False) for param in self.params]
         self.update_all_params()
     constrain_positive.__doc__ = Param.constrain_positive.__doc__
 
@@ -396,12 +413,12 @@ class ParamConcatenation(object):
     fix = constrain_fixed
 
     def constrain_negative(self, warning=True):
-        [param.constrain_negative(warning, update=False) for param in self.params]
+        [param.constrain_negative(warning, trigger_parent=False) for param in self.params]
         self.update_all_params()
     constrain_negative.__doc__ = Param.constrain_negative.__doc__
 
     def constrain_bounded(self, lower, upper, warning=True):
-        [param.constrain_bounded(lower, upper, warning, update=False) for param in self.params]
+        [param.constrain_bounded(lower, upper, warning, trigger_parent=False) for param in self.params]
         self.update_all_params()
     constrain_bounded.__doc__ = Param.constrain_bounded.__doc__
 

GPy/core/parameterization/parameter_core.py

@@ -6,6 +6,11 @@ import heapq
 
 __updated__ = '2013-12-16'
 
+class HierarchyError(Exception):
+    """
+    Gets thrown when something is wrong with the parameter hierarchy
+    """
+
 def adjust_name_for_printing(name):
     if name is not None:
         return name.replace(" ", "_").replace(".", "_").replace("-", "").replace("+", "").replace("!", "").replace("*", "").replace("/", "")
@@ -114,11 +119,11 @@ class Nameable(Parentable):
         self._name = name
         if self.has_parent():
             self._direct_parent_._name_changed(self, from_name)
-    def hirarchy_name(self, adjust_for_printing=True):
+    def hierarchy_name(self, adjust_for_printing=True):
         if adjust_for_printing: adjust = lambda x: adjust_name_for_printing(x)
         else: adjust = lambda x: x
         if self.has_parent():
-            return self._direct_parent_.hirarchy_name() + "." + adjust(self.name)
+            return self._direct_parent_.hierarchy_name() + "." + adjust(self.name)
         return adjust(self.name)
 
 
@@ -175,7 +180,7 @@ class Constrainable(Nameable, Indexable):
     #===========================================================================
     # Fixing Parameters:
     #===========================================================================
-    def constrain_fixed(self, value=None, warning=True):
+    def constrain_fixed(self, value=None, warning=True, trigger_parent=True):
         """
         Constrain this paramter to be fixed to the current value it carries.
 
@@ -183,7 +188,7 @@ class Constrainable(Nameable, Indexable):
         """
         if value is not None:
             self[:] = value
-        self.constrain(__fixed__, warning=warning)
+        self.constrain(__fixed__, warning=warning, trigger_parent=trigger_parent)
         rav_i = self._highest_parent_._raveled_index_for(self)
         self._highest_parent_._set_fixed(rav_i)
     fix = constrain_fixed
@@ -224,7 +229,7 @@ class Constrainable(Nameable, Indexable):
     #===========================================================================
     # Prior Operations
     #===========================================================================
-    def set_prior(self, prior, warning=True):
+    def set_prior(self, prior, warning=True, trigger_parent=True):
         repriorized = self.unset_priors()
         self._add_to_index_operations(self.priors, repriorized, prior, warning)
     
@@ -252,7 +257,7 @@ class Constrainable(Nameable, Indexable):
     # Constrain operations -> done
     #===========================================================================
 
-    def constrain(self, transform, warning=True):
+    def constrain(self, transform, warning=True, trigger_parent=True):
         """
         :param transform: the :py:class:`GPy.core.transformations.Transformation`
                           to constrain the this parameter to.
@@ -262,7 +267,7 @@ class Constrainable(Nameable, Indexable):
         :py:class:`GPy.core.transformations.Transformation`.
         """
         if isinstance(transform, Transformation):
-            self._set_params(transform.initialize(self._get_params()), trigger_parent=True)
+            self._set_params(transform.initialize(self._get_params()), trigger_parent=trigger_parent)
         reconstrained = self.unconstrain()
         self._add_to_index_operations(self.constraints, reconstrained, transform, warning)
 
@@ -275,30 +280,30 @@ class Constrainable(Nameable, Indexable):
         """
         return self._remove_from_index_operations(self.constraints, transforms)
     
-    def constrain_positive(self, warning=True):
+    def constrain_positive(self, warning=True, trigger_parent=True):
         """
         :param warning: print a warning if re-constraining parameters.
 
         Constrain this parameter to the default positive constraint.
         """
-        self.constrain(Logexp(), warning=warning)
+        self.constrain(Logexp(), warning=warning, trigger_parent=trigger_parent)
 
-    def constrain_negative(self, warning=True):
+    def constrain_negative(self, warning=True, trigger_parent=True):
         """
         :param warning: print a warning if re-constraining parameters.
 
         Constrain this parameter to the default negative constraint.
         """
-        self.constrain(NegativeLogexp(), warning=warning)
+        self.constrain(NegativeLogexp(), warning=warning, trigger_parent=trigger_parent)
 
-    def constrain_bounded(self, lower, upper, warning=True):
+    def constrain_bounded(self, lower, upper, warning=True, trigger_parent=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)
+        self.constrain(Logistic(lower, upper), warning=warning, trigger_parent=trigger_parent)
 
     def unconstrain_positive(self):
         """
@@ -359,6 +364,9 @@ class OptimizationHandlable(Constrainable, Observable):
         # inverse apply transformations for parameters and set the resulting parameters
         self._set_params(self._untransform_params(p))
     
+    def _size_transformed(self):
+        return self.size - self.constraints[__fixed__].size
+    
     def _untransform_params(self, p):
         p = p.copy()
         if self._has_fixes(): tmp = self._get_params(); tmp[self._fixes_] = p; p = tmp; del tmp
@@ -373,13 +381,13 @@ class OptimizationHandlable(Constrainable, Observable):
 
     def _set_params(self, params, trigger_parent=True):
         # don't overwrite this anymore!
-        raise NotImplementedError, "This needs to be implemented seperately"
+        raise NotImplementedError, "This needs to be implemented in Param and Parametrizable"
     
     #===========================================================================
     # 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()])
+        n = np.array([p.hierarchy_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()
@@ -398,7 +406,7 @@ class OptimizationHandlable(Constrainable, Observable):
         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 = 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]
@@ -435,7 +443,7 @@ class Parameterizable(OptimizationHandlable):
                 if pname in self._added_names_:
                     del self.__dict__[pname]
                     self._add_parameter_name(param)
-        else:
+        elif pname not in dir(self):
             self.__dict__[pname] = param
             self._added_names_.add(pname)
             

GPy/core/parameterization/parameterized.py

@@ -7,7 +7,7 @@ import cPickle
 import itertools
 from re import compile, _pattern_type
 from param import ParamConcatenation
-from parameter_core import Constrainable, Pickleable, Parentable, Observable, Parameterizable, adjust_name_for_printing, Gradcheckable
+from parameter_core import Pickleable, Parameterizable, adjust_name_for_printing, Gradcheckable
 from transformations import __fixed__
 from array_core import ParamList
 
@@ -105,6 +105,14 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable):
             self.remove_parameter(param)
             self.add_parameter(param, index)
         elif param not in self._parameters_:
+            if param.has_parent():
+                parent = param._direct_parent_
+                while parent is not None:
+                    if parent is self:
+                        from GPy.core.parameterization.parameter_core import HierarchyError
+                        raise HierarchyError, "You cannot add a parameter twice into the hirarchy"
+                    parent = parent._direct_parent_
+                param._direct_parent_.remove_parameter(param)
             # make sure the size is set
             if index is None:
                 self.constraints.update(param.constraints, self.size)
@@ -117,13 +125,16 @@ 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
+
+            self._connect_parameters()
+            self._notify_parent_change()
+            self._connect_fixes()
         else:
             raise RuntimeError, """Parameter exists already added and no copy made"""
-        self._connect_parameters()
-        self._notify_parent_change()
-        self._connect_fixes()
 
 
     def add_parameters(self, *parameters):
@@ -146,12 +157,19 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable):
         del self._parameters_[param._parent_index_]
         
         param._disconnect_parent()
-        param.remove_observer(self, self._notify_parameters_changed)
+        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._direct_parent_
+        while parent is not None:
+            parent._connect_fixes()
+            parent._connect_parameters()
+            parent._notify_parent_change()
+            parent = parent._direct_parent_
         
     def _connect_parameters(self):
         # connect parameterlist to this parameterized object
@@ -351,7 +369,7 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable):
     # Printing:
     #===========================================================================
     def _short(self):
-        return self.hirarchy_name()
+        return self.hierarchy_name()
     @property
     def flattened_parameters(self):
         return [xi for x in self._parameters_ for xi in x.flattened_parameters]
@@ -359,11 +377,6 @@ class Parameterized(Parameterizable, Pickleable, Gradcheckable):
     def _parameter_sizes_(self):
         return [x.size for x in self._parameters_]
     @property
-    def size_transformed(self):
-        if self._has_fixes():
-            return sum(self._fixes_)
-        return self.size
-    @property
     def parameter_shapes(self):
         return [xi for x in self._parameters_ for xi in x.parameter_shapes]
     @property

GPy/examples/dimensionality_reduction.py

@@ -187,10 +187,10 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False):
     _np.random.seed(1234)
     
     x = _np.linspace(0, 4 * _np.pi, N)[:, None]
-    s1 = _np.vectorize(lambda x: -_np.sin(_np.exp(x)))
+    s1 = _np.vectorize(lambda x: _np.sin(x))
     s2 = _np.vectorize(lambda x: _np.cos(x)**2)
     s3 = _np.vectorize(lambda x:-_np.exp(-_np.cos(2 * x)))
-    sS = _np.vectorize(lambda x: x*_np.sin(x))
+    sS = _np.vectorize(lambda x: _np.cos(x))
 
     s1 = s1(x)
     s2 = s2(x)
@@ -202,7 +202,7 @@ def _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim=False):
     s3 -= s3.mean(); s3 /= s3.std(0)
     sS -= sS.mean(); sS /= sS.std(0)
 
-    S1 = _np.hstack([s1, s2, sS])
+    S1 = _np.hstack([s1, sS])
     S2 = _np.hstack([s2, s3, sS])
     S3 = _np.hstack([s3, sS])
 
@@ -270,7 +270,7 @@ def bgplvm_simulation(optimize=True, verbose=1,
     from GPy import kern
     from GPy.models import BayesianGPLVM
 
-    D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 5, 9
+    D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 3, 9
     _, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
     Y = Ylist[0]
     k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
@@ -294,7 +294,7 @@ def bgplvm_simulation_missing_data(optimize=True, verbose=1,
     from GPy.models import BayesianGPLVM
     from GPy.inference.latent_function_inference.var_dtc import VarDTCMissingData
 
-    D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 5, 9
+    D1, D2, D3, N, num_inducing, Q = 13, 5, 8, 45, 7, 9
     _, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
     Y = Ylist[0]
     k = kern.Linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)

GPy/inference/latent_function_inference/var_dtc.py

@@ -60,8 +60,7 @@ class VarDTC(object):
         _, output_dim = Y.shape
 
         #see whether we've got a different noise variance for each datum
-        beta = 1./max(1e-6, np.squeeze(likelihood.variance))
+        beta = 1./np.fmax(likelihood.variance, 1e-6)
-
         # VVT_factor is a matrix such that tdot(VVT_factor) = VVT...this is for efficiency!
         #self.YYTfactor = self.get_YYTfactor(Y)
         #VVT_factor = self.get_VVTfactor(self.YYTfactor, beta)
@@ -214,7 +213,7 @@ class VarDTCMissingData(object):
             psi2_all = None
 
         Ys, traces = self._Y(Y)
-        beta_all = 1./max(1e-6, likelihood.variance)
+        beta_all = 1./np.fmax(likelihood.variance, 1e-6)
         het_noise = beta_all.size != 1
 
         import itertools

GPy/kern/_src/kern.py

@@ -112,10 +112,12 @@ class Kern(Parameterized):
         """
         assert isinstance(other, Kern), "only kernels can be added to kernels..."
         from add import Add
-        return Add([self, other], tensor)
+        kernels = []
-
+        if not tensor and isinstance(self, Add): kernels.extend(self._parameters_)
-    def __call__(self, X, X2=None):
+        else: kernels.append(self)
-        return self.K(X, X2)
+        if not tensor and isinstance(other, Add): kernels.extend(other._parameters_)
+        else: kernels.append(other)
+        return Add(kernels, tensor)
 
     def __mul__(self, other):
         """ Here we overload the '*' operator. See self.prod for more information"""

GPy/models/sparse_gp_regression.py

@@ -8,7 +8,7 @@ from .. import likelihoods
 from .. import kern
 from ..inference.latent_function_inference import VarDTC
 from ..util.misc import param_to_array
-from ..core.parameterization.variational import VariationalPosterior
+from ..core.parameterization.variational import NormalPosterior
 
 class SparseGPRegression(SparseGP):
     """
@@ -47,7 +47,7 @@ class SparseGPRegression(SparseGP):
         likelihood = likelihoods.Gaussian()
         
         if not (X_variance is None):
-            X = VariationalPosterior(X,X_variance)
+            X = NormalPosterior(X,X_variance)
         
         SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method=VarDTC())
 

GPy/testing/parameterized_tests.py

@@ -6,6 +6,7 @@ Created on Feb 13, 2014
 import unittest
 import GPy
 import numpy as np
+from GPy.core.parameterization.parameter_core import HierarchyError
 
 class Test(unittest.TestCase):
 
@@ -65,7 +66,7 @@ class Test(unittest.TestCase):
         self.assertListEqual(self.test1.constraints[Logexp()].tolist(), [0,1])
         
     def test_add_parameter_already_in_hirarchy(self):
-        self.test1.add_parameter(self.white._parameters_[0])
+        self.assertRaises(HierarchyError, self.test1.add_parameter, self.white._parameters_[0])        
         
     def test_default_constraints(self):
         self.assertIs(self.rbf.variance.constraints._param_index_ops, self.rbf.constraints._param_index_ops)

GPy/util/caching.py

@@ -40,7 +40,7 @@ class Cacher(object):
             return self.operation(*args)
 
         # TODO: WARNING !!! Cache OFFSWITCH !!! WARNING
-        # return self.operation(*args)
+        return self.operation(*args)
 
         #if the result is cached, return the cached computation
         state = [all(a is b for a, b in itertools.izip_longest(args, cached_i)) for cached_i in self.cached_inputs]