redid constraints

GPy/core/model.py

@@ -20,7 +20,7 @@ class Model(Parameterized):
     _fail_count = 0  # Count of failed optimization steps (see objective)
     _allowed_failures = 10  # number of allowed failures
     def __init__(self, name):
-        super(Model, self).__init__(name)#Parameterized.__init__(self)
+        super(Model, self).__init__(name)  # Parameterized.__init__(self)
         self.priors = []
         self._priors = ParameterIndexOperations()
         self.optimization_runs = []
@@ -30,10 +30,10 @@ class Model(Parameterized):
     def log_likelihood(self):
         raise NotImplementedError, "this needs to be implemented to use the model class"
     def _log_likelihood_gradients(self):
-        #def dK_d(self, param, dL_dK, X, X2)
+        # def dK_d(self, param, dL_dK, X, X2)
         g = np.zeros(self.size)
         try:
-            #[g.__setitem__(s, self.gradient_mapping[p]().flat) for p, s in itertools.izip(self._parameters_, self._param_slices_) if not p.is_fixed]
+            # [g.__setitem__(s, self.gradient_mapping[p]().flat) for p, s in itertools.izip(self._parameters_, self._param_slices_) if not p.is_fixed]
             [p._collect_gradient(g[s]) for p, s in itertools.izip(self._parameters_, self._param_slices_) if not p.is_fixed]
         except ValueError:
             raise ValueError, 'Gradient for {} not defined, please specify gradients for parameters to optimize'.format(p.name)
@@ -168,14 +168,14 @@ class Model(Parameterized):
         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 = self._get_params_transformed()
         x = np.random.randn(self.size_transformed)
         x = self._untransform_params(x)
         # now draw from prior where possible
         if self.priors is not None and len(self.priors):
             [np.put(x, i, p.rvs(1)) for i, p in enumerate(self.priors) 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...)
+        # 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):
         """
@@ -412,7 +412,7 @@ class Model(Parameterized):
             gradient = self.objective_function_gradients(x)
 
             numerical_gradient = (f1 - f2) / (2 * dx)
-            global_ratio = (f1 - f2) / (2 * np.dot(dx, np.where(gradient==0, 1e-32, gradient)))
+            global_ratio = (f1 - f2) / (2 * np.dot(dx, np.where(gradient == 0, 1e-32, gradient)))
 
             return (np.abs(1. - global_ratio) < tolerance) or (np.abs(gradient - numerical_gradient).mean() < tolerance)
         else:
@@ -444,18 +444,18 @@ class Model(Parameterized):
                     return
 
             gradient = self.objective_function_gradients(x)
-            np.where(gradient==0, 1e-312, gradient)
+            np.where(gradient == 0, 1e-312, gradient)
             ret = True
             for i, ind in enumerate(param_list):
                 xx = x.copy()
-                xx[ind] += step
+                xx[i] += step
                 f1 = self.objective_function(xx)
-                xx[ind] -= 2.*step
+                xx[i] -= 2.*step
                 f2 = self.objective_function(xx)
                 
                 numerical_gradient = (f1 - f2) / (2 * step)
-                ratio = (f1 - f2) / (2 * step * gradient[ind])
-                difference = np.abs((f1 - f2) / 2 / step - gradient[ind])
+                ratio = (f1 - f2) / (2 * step * gradient[i])
+                difference = np.abs((f1 - f2) / 2 / step - gradient[i])
 
                 if (np.abs(1. - ratio) < tolerance) or np.abs(difference) < tolerance:
                     formatted_name = "\033[92m {0} \033[0m".format(names[ind])
@@ -466,7 +466,7 @@ class Model(Parameterized):
 
                 r = '%.6f' % float(ratio)
                 d = '%.6f' % float(difference)
-                g = '%.6f' % gradient[ind]
+                g = '%.6f' % gradient[i]
                 ng = '%.6f' % float(numerical_gradient)
                 grad_string = "{0:<{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}".format(formatted_name, r, d, g, ng, c0=cols[0] + 9, c1=cols[1], c2=cols[2], c3=cols[3], c4=cols[4])
                 print grad_string
@@ -517,10 +517,10 @@ class Model(Parameterized):
         alpha = 0
         stop = False
 
-        #Handle **kwargs
+        # Handle **kwargs
         ep_args = {}
         for arg in kwargs.keys():
-            if arg in ('epsilon','power_ep'):
+            if arg in ('epsilon', 'power_ep'):
                 ep_args[arg] = kwargs[arg]
                 del kwargs[arg]
 
@@ -528,7 +528,7 @@ class Model(Parameterized):
             last_approximation = self.likelihood.copy()
             last_params = self._get_params()
             if len(ep_args) == 2:
-                self.update_likelihood_approximation(epsilon=ep_args['epsilon'],power_ep=ep_args['power_ep'])
+                self.update_likelihood_approximation(epsilon=ep_args['epsilon'], power_ep=ep_args['power_ep'])
             elif len(ep_args) == 1:
                 if  ep_args.keys()[0] == 'epsilon':
                     self.update_likelihood_approximation(epsilon=ep_args['epsilon'])

GPy/core/parameterization/array_core.py

@@ -4,7 +4,7 @@
 __updated__ = '2013-12-16'
 
 import numpy as np
-from parameter_core import Observable, Constrainable, Gradcheckable
+from parameter_core import Observable, Parameterizable
 
 class ParamList(list):
     """

GPy/core/parameterization/index_operations.py

@@ -58,7 +58,7 @@ class ParameterIndexOperations(object):
     index array, for multi-param handling.
     '''
     def __init__(self):
-        self._properties = ParamDict()
+        self._properties = IntArrayDict()
         #self._reverse = collections.defaultdict(list)
         
     def __getstate__(self):
@@ -71,16 +71,19 @@ class ParameterIndexOperations(object):
     def iteritems(self):
         return self._properties.iteritems()
     
+    def items(self):
+        return self._properties.items()
+
     def properties(self):
         return self._properties.keys()
 
-    def iter_properties(self):
+    def iterproperties(self):
         return self._properties.iterkeys()
     
     def shift(self, start, size):
         for ind in self.iterindices():
             toshift = ind>=start
-            if len(toshift) > 0:
+            if toshift.size > 0:
                 ind[toshift] += size
     
     def clear(self):
@@ -96,7 +99,7 @@ class ParameterIndexOperations(object):
         return self._properties.values()
 
     def properties_for(self, index):
-        return vectorize(lambda i: [prop for prop in self.iter_properties() if i in self._properties[prop]], otypes=[list])(index)
+        return vectorize(lambda i: [prop for prop in self.iterproperties() if i in self[prop]], otypes=[list])(index)
         
     def add(self, prop, indices):
         try:
@@ -114,9 +117,14 @@ class ParameterIndexOperations(object):
                 del self._properties[prop]
             return removed.astype(int)
         return numpy.array([]).astype(int)
+    
     def __getitem__(self, prop):
         return self._properties[prop]
     
+    def __str__(self, *args, **kwargs):
+        import pprint
+        return pprint.pformat(dict(self._properties))
+       
 def combine_indices(arr1, arr2):
     return numpy.union1d(arr1, arr2)
 
@@ -126,5 +134,94 @@ def remove_indices(arr, to_remove):
 def index_empty(index):
     return numpy.size(index) == 0 
 
+class ParameterIndexOperationsView(object):
+    def __init__(self, param_index_operations, offset, size):
+        self._param_index_ops = param_index_operations
+        self._offset = offset
+        self._size = size
+    
+    def __getstate__(self):
+        return [self._param_index_ops, self._offset, self._size]
 
 
+    def __setstate__(self, state):
+        self._param_index_ops = state[0]
+        self._offset = state[1]
+        self._size = state[2]
+
+
+    def _filter_index(self, ind):
+        return ind[(ind >= self._offset) * (ind < (self._offset + self._size))] - self._offset
+
+
+    def iteritems(self):
+        for i, ind in self._param_index_ops.iteritems():
+            ind2 = self._filter_index(ind)
+            if ind2.size > 0:
+                yield i, ind2 
+
+    def items(self):
+        return [[i,v] for i,v in self.iteritems()]
+
+    def properties(self):
+        return [i for i in self.iterproperties()]
+
+
+    def iterproperties(self):
+        for i, _ in self.iteritems():
+            yield i 
+
+
+    def shift(self, start, size):
+        raise NotImplementedError, 'Shifting only supported in original ParamIndexOperations'
+    
+
+    def clear(self):
+        for i, ind in self.items():
+            self._param_index_ops.remove(i, ind+self._offset)
+
+
+    def size(self):
+        return reduce(lambda a,b: a+b.size, self.iterindices(), 0)
+
+
+    def iterindices(self):
+        for _, ind in self.iteritems():
+            yield ind
+
+
+    def indices(self):
+        [ind for ind in self.iterindices()]
+
+
+    def properties_for(self, index):
+        return vectorize(lambda i: [prop for prop in self.iterproperties() if i in self[prop]], otypes=[list])(index)
+
+
+    def add(self, prop, indices):
+        self._param_index_ops.add(prop, indices+self._offset)
+
+
+    def remove(self, prop, indices):
+        removed = self._param_index_ops.remove(prop, indices+self._offset)
+        if removed.size > 0:
+            return removed - self._size
+        return removed
+
+
+    def __getitem__(self, prop):
+        ind = self._filter_index(self._param_index_ops[prop])
+        if ind.size > 0:
+            return ind
+        raise KeyError, prop
+    
+    def __str__(self, *args, **kwargs):
+        import pprint
+        return pprint.pformat(dict(self.iteritems()))
+
+    def update(self, parameter_index_view):
+        for i, v in parameter_index_view.iteritems():
+            self.add(i, v)
+        
+    pass
+

GPy/core/parameterization/param.py

@@ -3,7 +3,7 @@
 
 import itertools
 import numpy
-from parameter_core import Constrainable, Gradcheckable, adjust_name_for_printing
+from parameter_core import Constrainable, Gradcheckable, Indexable, Parameterizable, adjust_name_for_printing
 from array_core import ObservableArray, ParamList
 
 ###### printing
@@ -14,13 +14,7 @@ __precision__ = numpy.get_printoptions()['precision'] # numpy printing precision
 __print_threshold__ = 5
 ######
 
-class Float(numpy.float64, Constrainable):
-    def __init__(self, f, base):
-        super(Float,self).__init__(f)
-        self._base = base
-
-
-class Param(ObservableArray, Constrainable, Gradcheckable):
+class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parameterizable):
     """
     Parameter object for GPy models.
 
@@ -364,7 +358,7 @@ class Param(ObservableArray, Constrainable, Gradcheckable):
         return [self.shape]
     @property
     def _constraints_str(self):
-        return [' '.join(map(lambda c: str(c[0]) if c[1].size == self._realsize_ else "{" + str(c[0]) + "}", self._highest_parent_._constraints_iter_items(self)))]
+        return [' '.join(map(lambda c: str(c[0]) if c[1].size == self._realsize_ else "{" + str(c[0]) + "}", self.constraints.iteritems()))]
     @property
     def _ties_str(self):
         return [t._short() for t in self._tied_to_] or ['']
@@ -390,7 +384,7 @@ class Param(ObservableArray, Constrainable, Gradcheckable):
                     else: ties[i, matches[0]] = numpy.take(tt_rav_index, matches[1], mode='wrap')
         return map(lambda a: sum(a, []), zip(*[[[tie.flatten()] if tx != None else [] for tx in t] for t, tie in zip(ties, self._tied_to_)]))
     def _constraints_for(self, rav_index):
-        return self._highest_parent_._constraints_for(self, rav_index)
+        return self.constraints.properties_for(rav_index)
     def _indices(self, slice_index=None):
         # get a int-array containing all indices in the first axis.
         if slice_index is None:

GPy/core/parameterization/parameter_core.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2012, GPy authors (see AUTHORS.txt).
 # Licensed under the BSD 3-clause license (see LICENSE.txt)
 
-from transformations import Logexp, NegativeLogexp, Logistic
+from transformations import Transformation, Logexp, NegativeLogexp, Logistic
 
 __updated__ = '2013-12-16'
 
@@ -10,6 +10,11 @@ def adjust_name_for_printing(name):
         return name.replace(" ", "_").replace(".", "_").replace("-","").replace("+","").replace("!","").replace("*","").replace("/","")
     return ''
 
+#===============================================================================
+# Printing:
+__fixed__ = "fixed"
+#===============================================================================
+
 class Observable(object):
     _observers_ = {}
     def add_observer(self, observer, callble):
@@ -20,6 +25,22 @@ class Observable(object):
     def _notify_observers(self):
         [callble(self) for callble in self._observers_.itervalues()]
 
+class Parameterizable(object):
+    def __init__(self, *args, **kwargs):
+        from GPy.core.parameterization.array_core import ParamList
+        _parameters_ = ParamList()
+    
+    def parameter_names(self):
+        return [p.name for p in self._parameters_]
+    
+    def parameters_changed(self):
+        """
+        This method gets called when parameters have changed.
+        Another way of listening to param changes is to
+        add self as a listener to the param, such that
+        updates get passed through. See :py:function:``GPy.core.param.Observable.add_observer``
+        """
+        pass
     
 class Pickleable(object):
     def _getstate(self):
@@ -89,11 +110,22 @@ class Gradcheckable(Parentable):
     def _checkgrad(self, param):
         raise NotImplementedError, "Need log likelihood to check gradient against"
 
+class Indexable(object):
+    def _raveled_index(self):
+        raise NotImplementedError, "Need to be able to get the raveled Index"
         
-class Constrainable(Nameable):
+    def _internal_offset(self):
+        return 0
+    
+    def _offset_for(self, param):
+        raise NotImplementedError, "shouldnt happen, offset required from non parameterization object?"
+
+class Constrainable(Nameable, Indexable, Parameterizable):
     def __init__(self, name, default_constraint=None):
         super(Constrainable,self).__init__(name)
         self._default_constraint_ = default_constraint
+        from index_operations import ParameterIndexOperations
+        self.constraints = ParameterIndexOperations()
     #===========================================================================
     # Fixing Parameters:
     #===========================================================================
@@ -105,17 +137,28 @@ class Constrainable(Nameable):
         """
         if value is not None:
             self[:] = value
-        self._highest_parent_._fix(self,warning)
+        self.constrain(__fixed__, warning=warning)
+        self._highest_parent_._set_fixed(self._raveled_index())
     fix = constrain_fixed
     def unconstrain_fixed(self):
         """
         This parameter will no longer be fixed.
         """
-        self._highest_parent_._unfix(self)
+        unconstrained = self.unconstrain(__fixed__)
+        self._highest_parent_._set_unfixed(unconstrained)
     unfix = unconstrain_fixed
     #===========================================================================
     # Constrain operations -> done
     #===========================================================================
+    def _parent_changed(self, parent):
+        c = self.constraints
+        from index_operations import ParameterIndexOperationsView
+        self.constraints = ParameterIndexOperationsView(parent.constraints, parent._offset_for(self), self.size)
+        self.constraints.update(c)
+        del c
+        for p in self._parameters_:
+            p._parent_changed(parent)
+
     def constrain(self, transform, warning=True, update=True):
         """
         :param transform: the :py:class:`GPy.core.transformations.Transformation`
@@ -125,15 +168,21 @@ class Constrainable(Nameable):
         Constrain the parameter to the given
         :py:class:`GPy.core.transformations.Transformation`.
         """
-        if self.has_parent():
-            self._highest_parent_._add_constrain(self, transform, warning)
+        if isinstance(transform, Transformation):
+            self._set_params(transform.initialize(self._get_params()), update=False)
+        reconstrained = self.unconstrain()
+        self.constraints.add(transform, self._raveled_index())
+        if reconstrained.size > 0:
+            print "WARNING: reconstraining parameters {}".format(self.parameter_names)
         if update:
             self._highest_parent_.parameters_changed()
-        else:
-            for p in self._parameters_:
-                self._add_constrain(p, transform, warning)
-            if update:
-                self.parameters_changed()
+        # if self.has_parent():
+        #     self._highest_parent_._add_constrain(self, transform, warning)
+        # else:
+        #     for p in self._parameters_:
+        #         self._add_constrain(p, transform, warning)
+        #     if update:
+        #         self.parameters_changed()
 
     def constrain_positive(self, warning=True, update=True):
         """
@@ -167,11 +216,13 @@ class Constrainable(Nameable):
         remove all :py:class:`GPy.core.transformations.Transformation`
         transformats of this parameter object.
         """
-        if self.has_parent():
-            self._highest_parent_._remove_constrain(self, *transforms)
-        else:
-            for p in self._parameters_:
-                self._remove_constrain(p, *transforms)
+        if len(transforms) == 0:
+            transforms = self.constraints.properties()
+        import numpy as np
+        removed = np.empty((0,),dtype=int)
+        for t in transforms:
+            removed = np.intersect1d(removed, self.constraints.remove(t, self._raveled_index()))
+        return removed
     
     def unconstrain_positive(self):
         """

GPy/core/parameterization/parameterized.py

@@ -8,16 +8,9 @@ import cPickle
 import itertools
 from re import compile, _pattern_type
 from param import ParamConcatenation, Param
-from parameter_core import Constrainable, Pickleable, Observable, adjust_name_for_printing, Gradcheckable
-from index_operations import ParameterIndexOperations,\
-    index_empty
+from parameter_core import Constrainable, Pickleable, Observable, adjust_name_for_printing, Gradcheckable, __fixed__
 from array_core import ParamList
 
-#===============================================================================
-# Printing:
-__fixed__ = "fixed"
-#===============================================================================
-
 #===============================================================================
 # constants
 FIXED = False
@@ -69,7 +62,6 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
     def __init__(self, name=None):
         super(Parameterized, self).__init__(name=name)
         self._in_init_ = True
-        self._constraints_ = None#ParameterIndexOperations()
         self._parameters_ = ParamList()
         self.size = sum(p.size for p in self._parameters_)
         if not self._has_fixes():
@@ -79,11 +71,6 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
         self._added_names_ = set()
         del self._in_init_
 
-    @property
-    def constraints(self):
-        if self._constraints_ is None:
-            self._constraints_ = ParameterIndexOperations()
-        return self._constraints_
     #===========================================================================
     # Parameter connection for model creation:
     #===========================================================================
@@ -128,12 +115,14 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
         Add all parameters to this param class, you can insert parameters
         at any given index using the :func:`list.insert` syntax
         """
+        # if param.has_parent():
+        #    raise AttributeError, "parameter {} already in another model, create new object (or copy) for adding".format(param._short())
         if param in self._parameters_ and index is not None:
             # make sure fixes and constraints are indexed right
             if self._has_fixes():
-                param_slice = slice(self._offset_for(param),self._offset_for(param)+param.size)
+                param_slice = slice(self._offset_for(param), self._offset_for(param) + param.size)
                 dest_index = sum((p.size for p in self._parameters_[:index]))
-                dest_slice = slice(dest_index,dest_index+param.size)
+                dest_slice = slice(dest_index, dest_index + param.size)
                 fixes_param = self._fixes_[param_slice].copy()
                 self._fixes_[param_slice] = self._fixes_[dest_slice]
                 self._fixes_[dest_slice] = fixes_param
@@ -164,22 +153,18 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
                 ins = sum((p.size for p in self._parameters_[:index]))
                 if self._has_fixes(): self._fixes_ = np.r_[self._fixes_[:ins], fixes_param, self._fixes[ins:]]
                 elif not np.all(fixes_param):
-                    self._fixes_ = np.ones(self.size+param.size, dtype=bool)
-                    self._fixes_[ins:ins+param.size] = fixes_param
+                    self._fixes_ = np.ones(self.size + param.size, dtype=bool)
+                    self._fixes_[ins:ins + param.size] = fixes_param
             self.size += param.size
         else:
             raise RuntimeError, """Parameter exists already added and no copy made"""
         self._connect_parameters()
-        # make sure the constraints are pulled over:
-        if hasattr(param, "_constraints_") and param._constraints_ is not None:
-            for t, ind in param._constraints_.iteritems():
-                
-                self.constraints.add(t, ind+self._offset_for(param))
-            param._constraints_.clear()
+        for p in self._parameters_:
+            p._parent_changed(self)
         if param._default_constraint_ is not None:
-            self._add_constrain(param, param._default_constraint_, False)
+            param.constrain(param._default_constraint_, False)
         if self._has_fixes() and np.all(self._fixes_):  # ==UNFIXED
-            self._fixes_= None
+            self._fixes_ = None
 
     def add_parameters(self, *parameters):
         """
@@ -202,30 +187,22 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
                                 or p in names_params_indices)])
         self._connect_parameters()
 
-    def parameters_changed(self):
-        """
-        This method gets called when parameters have changed.
-        Another way of listening to param changes is to
-        add self as a listener to the param, such that
-        updates get passed through. See :py:function:``GPy.core.param.Observable.add_observer``
-        """
-        # will be called as soon as parameters have changed
-        pass
-
     def _connect_parameters(self):
         # connect parameterlist to this parameterized object
         # This just sets up the right connection for the params objects
         # to be used as parameters
+        # it also sets the constraints for each parameter to the constraints 
+        # of their respective parents 
         if not hasattr(self, "_parameters_") or len(self._parameters_) < 1:
             # no parameters for this class
             return
         sizes = [0]
         self._param_slices_ = []
-        for i,p in enumerate(self._parameters_):
+        for i, p in enumerate(self._parameters_):
             p._direct_parent_ = self
             p._parent_index_ = i
             not_unique = []
-            sizes.append(p.size+sizes[-1])
+            sizes.append(p.size + sizes[-1])
             self._param_slices_.append(slice(sizes[-2], sizes[-1]))
             pname = adjust_name_for_printing(p.name)
             # and makes sure to not delete programmatically added parameters
@@ -237,7 +214,6 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
             elif not (pname in not_unique):
                 self.__dict__[pname] = p
                 self._added_names_.add(pname)
-            
     #===========================================================================
     # Pickling operations
     #===========================================================================
@@ -255,16 +231,16 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
             cPickle.dump(self, f, protocol)
     def copy(self):
         """Returns a (deep) copy of the current model """
-        #dc = dict()
-        #for k, v in self.__dict__.iteritems():
-            #if k not in ['_highest_parent_', '_direct_parent_']:
-                #dc[k] = copy.deepcopy(v)
+        # dc = dict()
+        # for k, v in self.__dict__.iteritems():
+            # if k not in ['_highest_parent_', '_direct_parent_']:
+                # dc[k] = copy.deepcopy(v)
 
-        #dc = copy.deepcopy(self.__dict__)
-        #dc['_highest_parent_'] = None
-        #dc['_direct_parent_'] = None
-        #s = self.__class__.new()
-        #s.__dict__ = dc
+        # dc = copy.deepcopy(self.__dict__)
+        # dc['_highest_parent_'] = None
+        # dc['_direct_parent_'] = None
+        # s = self.__class__.new()
+        # s.__dict__ = dc
         return copy.deepcopy(self)
     def __getstate__(self):
         if self._has_get_set_state():
@@ -273,7 +249,7 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
     def __setstate__(self, state):
         if self._has_get_set_state():
             self._setstate(state)  # set state
-            #self._set_params(self._get_params()) # restore all values
+            # self._set_params(self._get_params()) # restore all values
             return
         self.__dict__ = state
     def _has_get_set_state(self):
@@ -289,7 +265,7 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
         """
         return [
                 self._fixes_,
-                self._constraints_,
+                self.constraints,
                 self._parameters_,
                 self._name,
                 self._added_names_,
@@ -300,7 +276,7 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
         self._name = state.pop()
         self._parameters_ = state.pop()
         self._connect_parameters()
-        self._constraints_ = state.pop()
+        self.constraints = state.pop()
         self._fixes_ = state.pop()
         self.parameters_changed()
     #===========================================================================
@@ -310,9 +286,9 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
         if self.has_parent():
             return g
         x = self._get_params()
-        [numpy.put(g, i, g[i]*c.gradfactor(x[i])) for c,i in self.constraints.iteritems() if c != __fixed__]
+        [numpy.put(g, i, g[i] * c.gradfactor(x[i])) for c, i in self.constraints.iteritems() if c != __fixed__]
         for p in self.flattened_parameters:
-            for t,i in p._tied_to_me_.iteritems():
+            for t, i in p._tied_to_me_.iteritems():
                 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
@@ -320,7 +296,7 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
     # Optimization handles:
     #===========================================================================
     def _get_param_names(self):
-        n = numpy.array([p.name_hirarchical+'['+str(i)+']' for p in self.flattened_parameters for i in p._indices()])
+        n = numpy.array([p.name_hirarchical + '[' + 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()
@@ -331,16 +307,16 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
         # don't overwrite this anymore!
         if not self.size:
             return np.empty(shape=(0,), dtype=np.float64)
-        return numpy.hstack([x._get_params() for x in self._parameters_ if x.size>0])
+        return numpy.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!
-        [p._set_params(params[s], update=update) for p,s in itertools.izip(self._parameters_,self._param_slices_)]
+        [p._set_params(params[s], update=update) for p, s in itertools.izip(self._parameters_, self._param_slices_)]
         self.parameters_changed()
     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__]
+        [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
@@ -350,7 +326,7 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
     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__]
+        [numpy.put(p, ind, c.f(p[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__]
         return p
     def _name_changed(self, param, old_name):
         if hasattr(self, old_name) and old_name in self._added_names_:
@@ -365,7 +341,7 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
     #===========================================================================
     def _backtranslate_index(self, param, ind):
         # translate an index in parameterized indexing into the index of param
-        ind = ind-self._offset_for(param)
+        ind = ind - self._offset_for(param)
         ind = ind[ind >= 0]
         internal_offset = param._internal_offset()
         ind = ind[ind < param.size + internal_offset]
@@ -420,13 +396,13 @@ class Parameterized(Constrainable, Pickleable, Observable, 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)]
-    def _fix(self, param, warning=True):
-        f = self._add_constrain(param, __fixed__, warning)
-        self._set_fixed(f)
-    def _unfix(self, param):
-        if self._has_fixes():
-            f = self._remove_constrain(param, __fixed__)
-            self._set_unfixed(f)
+    # def _fix(self, param, warning=True):
+    #     f = self._add_constrain(param, __fixed__, warning)
+    #     self._set_fixed(f)
+    # def _unfix(self, param):
+    #     if self._has_fixes():
+    #         f = self._remove_constrain(param, __fixed__)
+    #         self._set_unfixed(f)
     #===========================================================================
     # Convenience for fixed, tied checking of param:
     #===========================================================================
@@ -437,7 +413,7 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
         if not self._has_fixes():
             return False
         return not self._fixes_[self._raveled_index_for(param)].any()
-        #return not self._fixes_[self._offset_for(param): self._offset_for(param)+param._realsize_].any()
+        # return not self._fixes_[self._offset_for(param): self._offset_for(param)+param._realsize_].any()
     @property
     def is_fixed(self):
         for p in self._parameters_:
@@ -455,54 +431,33 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
     #===========================================================================
     # Constraint Handling:
     #===========================================================================
-    def _add_constrain(self, param, transform, warning=True):
-        rav_i = self._raveled_index_for(param)
-        reconstrained = self._remove_constrain(param, index=rav_i) # remove constraints before
-        # if removing constraints before adding new is not wanted, just delete the above line!
-        self.constraints.add(transform, rav_i)
-        param = self._get_original(param)
-        if not (transform == __fixed__):
-            param._set_params(transform.initialize(param._get_params()), update=False)
-        if warning and any(reconstrained):
-            # if you want to print the whole params object, which was reconstrained use:
-            # m = str(param[self._backtranslate_index(param, reconstrained)])
-            print "Warning: re-constraining parameters:\n{}".format(param._short())
-        return rav_i
-    def _remove_constrain(self, param, *transforms, **kwargs):
-        if not transforms:
-            transforms = self.constraints.properties()
-        removed_indices = numpy.array([]).astype(int)
-        if "index" in kwargs: index = kwargs['index']
-        else: index = self._raveled_index_for(param)
-        for constr in transforms:
-            removed = self.constraints.remove(constr, index)
-            if constr is __fixed__:
-                self._set_unfixed(removed)
-            removed_indices = numpy.union1d(removed_indices, removed)
-        return removed_indices
-    # convienience for iterating over items
-    def _constraints_iter_items(self, param):
-        for constr, ind in self.constraints.iteritems():
-            ind = self._backtranslate_index(param, ind)
-            if not index_empty(ind):
-                yield constr, ind
-    def _constraints_iter(self, param):
-        for constr, _ in self._constraints_iter_items(param):
-            yield constr
-    def _contraints_iter_indices(self, param):
-        # iterate through all constraints belonging to param
-        for _, ind in self._constraints_iter_items(param):
-            yield ind
-    def _constraint_indices(self, param, constraint):
-        # indices in model range for param and constraint
-        return self._backtranslate_index(param, self.constraints[constraint]) + self._offset_for(param)
-    def _constraints_for(self, param, rav_index):
-        # constraint for param given its internal rav_index
-        return self.constraints.properties_for(rav_index+self._offset_for(param))
-    def _constraints_for_collect(self, param, rav_index):
-        # constraint for param given its internal rav_index
-        cs = self._constraints_for(param, rav_index)
-        return set(itertools.chain(*cs))
+    #===========================================================================
+    # def _add_constrain(self, param, transform, warning=True):
+    #     rav_i = self._raveled_index_for(param)
+    #     reconstrained = self._remove_constrain(param, index=rav_i) # remove constraints before
+    #     # if removing constraints before adding new is not wanted, just delete the above line!
+    #     self.constraints.add(transform, rav_i)
+    #     param = self._get_original(param)
+    #     if not (transform == __fixed__):
+    #         param._set_params(transform.initialize(param._get_params()), update=False)
+    #     if warning and any(reconstrained):
+    #         # if you want to print the whole params object, which was reconstrained use:
+    #         # m = str(param[self._backtranslate_index(param, reconstrained)])
+    #         print "Warning: re-constraining parameters:\n{}".format(param._short())
+    #     return rav_i
+    # def _remove_constrain(self, param, *transforms, **kwargs):
+    #     if not transforms:
+    #         transforms = self.constraints.properties()
+    #     removed_indices = numpy.array([]).astype(int)
+    #     if "index" in kwargs: index = kwargs['index']
+    #     else: index = self._raveled_index_for(param)
+    #     for constr in transforms:
+    #         removed = self.constraints.remove(constr, index)
+    #         if constr is __fixed__:
+    #             self._set_unfixed(removed)
+    #         removed_indices = numpy.union1d(removed_indices, removed)
+    #     return removed_indices
+    #===========================================================================
     #===========================================================================
     # Get/set parameters:
     #===========================================================================
@@ -539,7 +494,7 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
 #     def __getattribute__(self, name):
 #         #try:
 #             return object.__getattribute__(self, name)
-        #except AttributeError:
+        # except AttributeError:
         #    _, a, tb = sys.exc_info()
         #    try:
         #        return self.__getitem__(name)
@@ -603,11 +558,11 @@ class Parameterized(Constrainable, Pickleable, Observable, Gradcheckable):
         to_print = []
         for n, d, c, t in itertools.izip(names, desc, constrs, ts):
             to_print.append(format_spec.format(name=n, desc=d, const=c, t=t))
-        #to_print = [format_spec.format(p=p, const=c, t=t) if isinstance(p, Param) else p.__str__(header=False) for p, c, t in itertools.izip(self._parameters_, constrs, ts)]
-        sep = '-'*(nl+sl+cl+tl+8*2+3)
+        # to_print = [format_spec.format(p=p, const=c, t=t) if isinstance(p, Param) else p.__str__(header=False) for p, c, t in itertools.izip(self._parameters_, constrs, ts)]
+        sep = '-' * (nl + sl + cl + tl + 8 * 2 + 3)
         if header:
             header = "  {{0:<{0}s}}  |  {{1:^{1}s}}  |  {{2:^{2}s}}  |  {{3:^{3}s}}".format(nl, sl, cl, tl).format(name, "Value", "Constraint", "Tied to")
-            #header += '\n' + sep
+            # header += '\n' + sep
             to_print.insert(0, header)
         return '\n'.format(sep).join(to_print)
     pass

GPy/core/parameterization/transformations.py

@@ -27,6 +27,8 @@ class Transformation(object):
         raise NotImplementedError
     def __str__(self):
         raise NotImplementedError
+    def __repr__(self):
+        return self.__class__.__name__
 
 class Logexp(Transformation):
     domain = _POSITIVE
@@ -94,7 +96,6 @@ class LogexpClipped(Logexp):
     def __str__(self):
         return '+ve_c'
 
-
 class Exponent(Transformation):
     # TODO: can't allow this to go to zero, need to set a lower bound. Similar with negative Exponent below. See old MATLAB code.
     domain = _POSITIVE

GPy/models/bayesian_gplvm.py

@@ -23,7 +23,7 @@ class BayesianGPLVM(SparseGP, GPLVM):
 
     """
     def __init__(self, Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
-                 Z=None, kernel=None, inference_method=None, likelihood=Gaussian(), name='bayesian gplvm', **kwargs):
+                 Z=None, kernel=None, inference_method=None, likelihood=None, name='bayesian gplvm', **kwargs):
         if X == None:
             X = self.initialise_latent(init, input_dim, Y)
         self.init = init
@@ -38,6 +38,8 @@ class BayesianGPLVM(SparseGP, GPLVM):
         if kernel is None:
             kernel = kern.rbf(input_dim) # + kern.white(input_dim)
         
+        if likelihood is None:
+            likelihood = Gaussian()
         self.q = Normal(X, X_variance)
         SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method, X_variance, name, **kwargs)
         self.add_parameter(self.q, index=0)