mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-11 21:12:38 +02:00
exact gaussian inference now accepts a kern and X, not K
This commit is contained in:
James Hensman
parent
206ec5dfc1
commit
e94b16e0b3
4 changed files with 58 additions and 519 deletions
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@ -60,9 +60,7 @@ class GP(Model):
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self.parameters_changed()
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def parameters_changed(self):
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super(GP, self).parameters_changed()
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self.K = self.kern.K(self.X)
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self.posterior = self.inference_method.inference(self.K, self.likelihood, self.Y)
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self.posterior = self.inference_method.inference(self.kern, self.X, self.likelihood, self.Y)
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def log_likelihood(self):
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return self.posterior.log_marginal
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@ -38,7 +38,7 @@ class Model(Parameterized):
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raise KeyError, 'Gradient for {} not defined, please specify gradients for parameters to optimize'.format(p.name)
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return g
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raise NotImplementedError, "this needs to be implemented to use the model class"
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def getstate(self):
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"""
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Get the current state of the class.
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@ -26,43 +26,43 @@ UNFIXED = True
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class Parameterized(Constrainable, Pickleable, Observable):
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"""
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Parameterized class
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Say m is a handle to a parameterized class.
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Printing parameters:
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- print m: prints a nice summary over all parameters
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- print m.name: prints details for param with name 'name'
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- print m[regexp]: prints details for all the parameters
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- print m[regexp]: prints details for all the parameters
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which match (!) regexp
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- print m['']: prints details for all parameters
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Fields:
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Name: The name of the param, can be renamed!
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Value: Shape or value, if one-valued
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Constrain: constraint of the param, curly "{c}" brackets indicate
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Constrain: constraint of the param, curly "{c}" brackets indicate
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some parameters are constrained by c. See detailed print
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to get exact constraints.
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Tied_to: which paramter it is tied to.
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Getting and setting parameters:
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Set all values in param to one:
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m.name.to.param = 1
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Handling of constraining, fixing and tieing parameters:
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You can constrain parameters by calling the constrain on the param itself, e.g:
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- m.name[:,1].constrain_positive()
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- m.name[0].tie_to(m.name[1])
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Fixing parameters will fix them to the value they are right now. If you change
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the parameters value, the param will be fixed to the new value!
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If you want to operate on all parameters use m[''] to wildcard select all paramters
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If you want to operate on all parameters use m[''] to wildcard select all paramters
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and concatenate them. Printing m[''] will result in printing of all parameters in detail.
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"""
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def __init__(self, name=None):
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@ -96,18 +96,18 @@ class Parameterized(Constrainable, Pickleable, Observable):
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# :param gradient: gradient method of the param
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# :type gradient: callable
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# :param index: (optional) index of the param when printing
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#
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#
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# (:param gradient_parent: connect these parameters to this class, but tell
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# updates to highest_parent, this is needed when parameterized classes
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# contain parameterized classes, but want to access the parameters
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# of their children)
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#
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#
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# of their children)
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#
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#
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# Set array (e.g. self.X) as param with name and gradient.
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# I.e: self.set_as_parameter('curvature', self.lengthscale, self.dK_dlengthscale)
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#
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# Note: the order in which parameters are added can be adjusted by
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# giving an index, of where to put this param in printing
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#
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# Note: the order in which parameters are added can be adjusted by
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# giving an index, of where to put this param in printing
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# """
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# if index is None:
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# self._parameters_.append(Param(name, array, gradient))
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@ -122,13 +122,13 @@ class Parameterized(Constrainable, Pickleable, Observable):
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"""
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:param parameters: the parameters to add
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:type parameters: list of or one :py:class:`GPy.core.param.Param`
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:param [gradients]: gradients for each param,
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:param [gradients]: gradients for each param,
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one gradient per param
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:param [index]: index of where to put parameters
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Add all parameters to this param class, you can insert parameters
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at any given index using the :func:`list.insert` syntax
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Add all parameters to this param class, you can insert parameters
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at any given index using the :func:`list.insert` syntax
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"""
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if param in self._parameters_ and index is not None:
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# make sure fixes and constraints are indexed right
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@ -139,7 +139,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
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fixes_param = self._fixes_[param_slice].copy()
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self._fixes_[param_slice] = self._fixes_[dest_slice]
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self._fixes_[dest_slice] = fixes_param
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del self._parameters_[param._parent_index_]
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self._parameters_.insert(index, param)
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elif param not in self._parameters_:
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@ -148,70 +148,70 @@ class Parameterized(Constrainable, Pickleable, Observable):
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self.size = sum(p.size for p in self._parameters_)
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if index is None:
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self._parameters_.append(param)
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# make sure fixes and constraints are indexed right
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if param._has_fixes(): fixes_param = param._fixes_.copy()
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else: fixes_param = numpy.ones(param.size, dtype=bool)
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if self._has_fixes(): self._fixes_ = np.r_[self._fixes_, fixes_param]
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elif param._has_fixes(): self._fixes_ = np.r_[np.ones(self.size, dtype=bool), fixes_param]
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else:
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self._parameters_.insert(index, param)
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# make sure fixes and constraints are indexed right
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if param._has_fixes(): fixes_param = param._fixes_.copy()
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else: fixes_param = numpy.ones(param.size, dtype=bool)
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ins = sum((p.size for p in self._parameters_[:index]))
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if self._has_fixes(): self._fixes_ = np.r_[self._fixes_[:ins], fixes_param, self._fixes[ins:]]
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elif not np.all(fixes_param):
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elif not np.all(fixes_param):
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self._fixes_ = np.ones(self.size+param.size, dtype=bool)
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self._fixes_[ins:ins+param.size] = fixes_param
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self.size += param.size
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if gradient:
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self.gradient_mapping[param] = gradient
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self.gradient_mapping[param] = gradient
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self._connect_parameters()
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# make sure the constraints are pulled over:
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if hasattr(param, "_constraints_") and param._constraints_ is not None:
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for t, ind in param._constraints_.iteritems():
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self.constraints.add(t, ind+self._offset_for(param))
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param._constraints_.clear()
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param._constraints_.clear()
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if self._has_fixes() and np.all(self._fixes_): # ==UNFIXED
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self._fixes_= None
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def add_parameters(self, *parameters):
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"""
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convenience method for adding several
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convenience method for adding several
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parameters without gradient specification
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"""
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[self.add_parameter(p) for p in parameters]
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def remove_parameter(self, *names_params_indices):
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"""
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:param names_params_indices: mix of parameter_names, param objects, or indices
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to remove from being a param of this parameterized object.
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:param names_params_indices: mix of parameter_names, param objects, or indices
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to remove from being a param of this parameterized object.
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note: if it is a string object it will not (!) be regexp-matched
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automatically.
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"""
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self._parameters_ = [p for p in self._parameters_
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if not (p._parent_index_ in names_params_indices
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self._parameters_ = [p for p in self._parameters_
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if not (p._parent_index_ in names_params_indices
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or p.name in names_params_indices
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or p in names_params_indices)]
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self._connect_parameters()
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def parameters_changed(self):
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"""
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This method gets called when parameters have changed.
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Another way of listening to param changes is to
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add self as a listener to the param, such that
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This method gets called when parameters have changed.
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Another way of listening to param changes is to
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add self as a listener to the param, such that
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updates get passed through. See :py:function:``GPy.core.param.Observable.add_observer``
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"""
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# will be called as soon as paramters have changed
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pass
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def _connect_parameters(self):
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# connect parameterlist to this parameterized object
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# This just sets up the right connection for the params objects
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# This just sets up the right connection for the params objects
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# to be used as parameters
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if not hasattr(self, "_parameters_") or len(self._parameters_) < 1:
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# no parameters for this class
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@ -228,7 +228,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
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not_unique = []
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sizes.append(p.size+sizes[-1])
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self._param_slices_.append(slice(sizes[-2], sizes[-1]))
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pname = adjust_name_for_printing(p.name)
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pname = adjust_name_for_printing(p.name)
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# and makes sure to not delete programmatically added parameters
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if pname in self.__dict__:
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if isinstance(self.__dict__[pname], (Parameterized, Param)):
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@ -273,7 +273,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
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Get the current state of the class,
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here just all the indices, rest can get recomputed
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For inheriting from Parameterized:
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Allways append the state of the inherited object
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and call down to the inherited object in setstate!!
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"""
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@ -285,7 +285,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
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#self.gradient_mapping,
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self._added_names_,
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]
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def setstate(self, state):
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self._added_names_ = state.pop()
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#self.gradient_mapping = state.pop()
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@ -361,7 +361,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
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return self._param_slices_[param._direct_parent_._get_original(param)._parent_index_].start
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return self._offset_for(param._direct_parent_) + param._direct_parent_._offset_for(param)
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return 0
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def _raveled_index_for(self, param):
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"""
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get the raveled index for a param
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@ -369,7 +369,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
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param inside this parameterized logic.
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"""
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return param._raveled_index() + self._offset_for(param)
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def _raveled_index(self):
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"""
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get the raveled index for this object,
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@ -441,7 +441,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
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def _add_constrain(self, param, transform, warning=True):
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rav_i = self._raveled_index_for(param)
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reconstrained = self._remove_constrain(param, index=rav_i) # remove constraints before
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# if removing constraints before adding new is not wanted, just delete the above line!
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# if removing constraints before adding new is not wanted, just delete the above line!
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self.constraints.add(transform, rav_i)
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param = self._get_original(param)
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param._set_params(transform.initialize(param._get_params()))
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@ -454,7 +454,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
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if not transforms:
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transforms = self.constraints.properties()
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removed_indices = numpy.array([]).astype(int)
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if "index" in kwargs: index = kwargs['index']
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if "index" in kwargs: index = kwargs['index']
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else: index = self._raveled_index_for(param)
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for constr in transforms:
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removed = self.constraints.remove(constr, index)
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@ -505,7 +505,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
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if paramlist is None:
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paramlist = self.grep_param_names(name)
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if len(paramlist) < 1: raise AttributeError, name
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if len(paramlist) == 1:
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if len(paramlist) == 1:
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if isinstance(paramlist[-1], Parameterized):
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paramlist = paramlist[-1].flattened_parameters
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if len(paramlist) != 1:
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@ -519,7 +519,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
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# def __getattr__(self, name):
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# return self.__getitem__(name)
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# def __getattribute__(self, name):
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# #try:
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# #try:
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# return object.__getattribute__(self, name)
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#except AttributeError:
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# _, a, tb = sys.exc_info()
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@ -571,7 +571,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
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def _ties_str(self):
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return [','.join(x._ties_str) for x in self.flattened_parameters]
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def __str__(self, header=True):
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name = adjust_name_for_printing(self.name) + "."
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constrs = self._constraints_str; ts = self._ties_str
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desc = self._description_str; names = self.parameter_names
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@ -592,463 +592,4 @@ class Parameterized(Constrainable, Pickleable, Observable):
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return '\n'.format(sep).join(to_print)
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pass
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#
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# class Parameterized_old(object):
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# def __init__(self):
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# """
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# This is the base class for model and kernel. Mostly just handles tieing and constraining of parameters
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# """
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# self.tied_indices = []
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# self.fixed_indices = []
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# self.fixed_values = []
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# self.constrained_indices = []
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# self.constraints = []
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#
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# def _get_params(self):
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# raise NotImplementedError, "this needs to be implemented to use the Parameterized class"
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# def _set_params(self, x):
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# raise NotImplementedError, "this needs to be implemented to use the Parameterized class"
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#
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# def _get_param_names(self):
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# raise NotImplementedError, "this needs to be implemented to use the Parameterized class"
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# #def _get_print_names(self):
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# # """ Override for which parameter_names to print out, when using print m """
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# # return self._get_param_names()
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#
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# def pickle(self, filename, protocol=None):
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# if protocol is None:
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# if self._has_get_set_state():
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# protocol = 0
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# else:
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# protocol = -1
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# with open(filename, 'w') as f:
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# cPickle.dump(self, f, protocol)
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#
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# def copy(self):
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# """Returns a (deep) copy of the current model """
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# return copy.deepcopy(self)
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#
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# def __getstate__(self):
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# if self._has_get_set_state():
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# return self.getstate()
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# return self.__dict__
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#
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# def __setstate__(self, state):
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# if self._has_get_set_state():
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# self.setstate(state) # set state
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# self._set_params(self._get_params()) # restore all values
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# return
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# self.__dict__ = state
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#
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# def _has_get_set_state(self):
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# return 'getstate' in vars(self.__class__) and 'setstate' in vars(self.__class__)
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#
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# def getstate(self):
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# """
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# Get the current state of the class,
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# here just all the indices, rest can get recomputed
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# For inheriting from Parameterized:
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#
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# Allways append the state of the inherited object
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# and call down to the inherited object in setstate!!
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# """
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# return [self.tied_indices,
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# self.fixed_indices,
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# self.fixed_values,
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# self.constrained_indices,
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# self.constraints]
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#
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# def setstate(self, state):
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# self.constraints = state.pop()
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# self.constrained_indices = state.pop()
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# self.fixed_values = state.pop()
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# self.fixed_indices = state.pop()
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# self.tied_indices = state.pop()
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#
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# def __getitem__(self, regexp, return_names=False):
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# """
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# Get a model param by name. The name is applied as a regular
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# expression and all parameters that match that regular expression are
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# returned.
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# """
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# matches = self.grep_param_names(regexp)
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# if len(matches):
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# if return_names:
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# return self._get_params()[matches], np.asarray(self._get_param_names())[matches].tolist()
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# else:
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# return self._get_params()[matches]
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# else:
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# raise AttributeError, "no param matches %s" % regexp
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#
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# def __setitem__(self, name, val):
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# """
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# Set model param(s) by name. The name is provided as a regular
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# expression. All parameters matching that regular expression are set to
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# the given value.
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# """
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# matches = self.grep_param_names(name)
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# if len(matches):
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# val = np.array(val)
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# assert (val.size == 1) or val.size == len(matches), "Shape mismatch: {}:({},)".format(val.size, len(matches))
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# x = self._get_params()
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# x[matches] = val
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# self._set_params(x)
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# else:
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# raise AttributeError, "no param matches %s" % name
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#
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# def tie_params(self, regexp):
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# """
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# Tie (all!) parameters matching the regular expression `regexp`.
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# """
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# matches = self.grep_param_names(regexp)
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# assert matches.size > 0, "need at least something to tie together"
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# if len(self.tied_indices):
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# assert not np.any(matches[:, None] == np.hstack(self.tied_indices)), "Some indices are already tied!"
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# self.tied_indices.append(matches)
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# # TODO only one of the priors will be evaluated. Give a warning message if the priors are not identical
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# if hasattr(self, 'prior'):
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# pass
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#
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# self._set_params_transformed(self._get_params_transformed()) # sets tied parameters to single value
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#
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# def untie_everything(self):
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# """Unties all parameters by setting tied_indices to an empty list."""
|
||||
# self.tied_indices = []
|
||||
#
|
||||
# def grep_param_names(self, regexp, transformed=False, search=False):
|
||||
# """
|
||||
# :param regexp: regular expression to select param parameter_names
|
||||
# :type regexp: re | str | int
|
||||
# :rtype: the indices of self._get_param_names which match the regular expression.
|
||||
#
|
||||
# Note:-
|
||||
# Other objects are passed through - i.e. integers which weren't meant for grepping
|
||||
# """
|
||||
#
|
||||
# if transformed:
|
||||
# parameter_names = self._get_param_names_transformed()
|
||||
# else:
|
||||
# parameter_names = self._get_param_names()
|
||||
#
|
||||
# if type(regexp) in [str, np.string_, np.str]:
|
||||
# regexp = re.compile(regexp)
|
||||
# elif type(regexp) is re._pattern_type:
|
||||
# pass
|
||||
# else:
|
||||
# return regexp
|
||||
# if search:
|
||||
# return np.nonzero([regexp.search(name) for name in parameter_names])[0]
|
||||
# else:
|
||||
# return np.nonzero([regexp.match(name) for name in parameter_names])[0]
|
||||
#
|
||||
# def num_params_transformed(self):
|
||||
# removed = 0
|
||||
# for tie in self.tied_indices:
|
||||
# removed += tie.size - 1
|
||||
#
|
||||
# for fix in self.fixed_indices:
|
||||
# removed += fix.size
|
||||
#
|
||||
# return len(self._get_params()) - removed
|
||||
#
|
||||
# def unconstrain(self, regexp):
|
||||
# """Unconstrain matching parameters. Does not untie parameters"""
|
||||
# matches = self.grep_param_names(regexp)
|
||||
#
|
||||
# # tranformed contraints:
|
||||
# for match in matches:
|
||||
# self.constrained_indices = [i[i <> match] for i in self.constrained_indices]
|
||||
#
|
||||
# # remove empty constraints
|
||||
# tmp = zip(*[(i, t) for i, t in zip(self.constrained_indices, self.constraints) if len(i)])
|
||||
# if tmp:
|
||||
# self.constrained_indices, self.constraints = zip(*[(i, t) for i, t in zip(self.constrained_indices, self.constraints) if len(i)])
|
||||
# self.constrained_indices, self.constraints = list(self.constrained_indices), list(self.constraints)
|
||||
#
|
||||
# # fixed:
|
||||
# self.fixed_values = [np.delete(values, np.nonzero(np.sum(indices[:, None] == matches[None, :], 1))[0]) for indices, values in zip(self.fixed_indices, self.fixed_values)]
|
||||
# self.fixed_indices = [np.delete(indices, np.nonzero(np.sum(indices[:, None] == matches[None, :], 1))[0]) for indices in self.fixed_indices]
|
||||
#
|
||||
# # remove empty elements
|
||||
# tmp = [(i, v) for i, v in zip(self.fixed_indices, self.fixed_values) if len(i)]
|
||||
# if tmp:
|
||||
# self.fixed_indices, self.fixed_values = zip(*tmp)
|
||||
# self.fixed_indices, self.fixed_values = list(self.fixed_indices), list(self.fixed_values)
|
||||
# else:
|
||||
# self.fixed_indices, self.fixed_values = [], []
|
||||
#
|
||||
# def constrain_negative(self, regexp, warning=True):
|
||||
# """ Set negative constraints. """
|
||||
# self.constrain(regexp, transformations.NegativeLogexp(), warning)
|
||||
#
|
||||
# def constrain_positive(self, regexp, warning=True):
|
||||
# """ Set positive constraints. """
|
||||
# self.constrain(regexp, transformations.Logexp(), warning)
|
||||
#
|
||||
# def constrain_bounded(self, regexp, lower, upper, warning=True):
|
||||
# """ Set bounded constraints. """
|
||||
# self.constrain(regexp, transformations.Logistic(lower, upper), warning)
|
||||
#
|
||||
# def all_constrained_indices(self):
|
||||
# if len(self.constrained_indices) or len(self.fixed_indices):
|
||||
# return np.hstack(self.constrained_indices + self.fixed_indices)
|
||||
# else:
|
||||
# return np.empty(shape=(0,))
|
||||
#
|
||||
# def constrain(self, regexp, transform, warning=True):
|
||||
# assert isinstance(transform, transformations.Transformation)
|
||||
#
|
||||
# matches = self.grep_param_names(regexp)
|
||||
# overlap = set(matches).intersection(set(self.all_constrained_indices()))
|
||||
# if overlap:
|
||||
# self.unconstrain(np.asarray(list(overlap)))
|
||||
# if warning:
|
||||
# print 'Warning: re-constraining these parameters'
|
||||
# pn = self._get_param_names()
|
||||
# for i in overlap:
|
||||
# print pn[i]
|
||||
#
|
||||
# self.constrained_indices.append(matches)
|
||||
# self.constraints.append(transform)
|
||||
# x = self._get_params()
|
||||
# x[matches] = transform.initialize(x[matches])
|
||||
# self._set_params(x)
|
||||
#
|
||||
# def constrain_fixed(self, regexp, value=None, warning=True):
|
||||
# """
|
||||
#
|
||||
# :param regexp: which parameters need to be fixed.
|
||||
# :type regexp: ndarray(dtype=int) or regular expression object or string
|
||||
# :param value: the vlaue to fix the parameters to. If the value is not specified,
|
||||
# the param is fixed to the current value
|
||||
# :type value: float
|
||||
#
|
||||
# **Notes**
|
||||
#
|
||||
# Fixing a param which is tied to another, or constrained in some way will result in an error.
|
||||
#
|
||||
# To fix multiple parameters to the same value, simply pass a regular expression which matches both param parameter_names, or pass both of the indexes.
|
||||
#
|
||||
# """
|
||||
# matches = self.grep_param_names(regexp)
|
||||
# overlap = set(matches).intersection(set(self.all_constrained_indices()))
|
||||
# if overlap:
|
||||
# self.unconstrain(np.asarray(list(overlap)))
|
||||
# if warning:
|
||||
# print 'Warning: re-constraining these parameters'
|
||||
# pn = self._get_param_names()
|
||||
# for i in overlap:
|
||||
# print pn[i]
|
||||
#
|
||||
# self.fixed_indices.append(matches)
|
||||
# if value != None:
|
||||
# self.fixed_values.append(value)
|
||||
# else:
|
||||
# self.fixed_values.append(self._get_params()[self.fixed_indices[-1]])
|
||||
#
|
||||
# # self.fixed_values.append(value)
|
||||
# self._set_params_transformed(self._get_params_transformed())
|
||||
#
|
||||
# def _get_params_transformed(self):
|
||||
# """use self._get_params to get the 'true' parameters of the model, which are then tied, constrained and fixed"""
|
||||
# x = self._get_params()
|
||||
# [np.put(x, i, t.finv(x[i])) for i, t in zip(self.constrained_indices, self.constraints)]
|
||||
#
|
||||
# to_remove = self.fixed_indices + [t[1:] for t in self.tied_indices]
|
||||
# if len(to_remove):
|
||||
# return np.delete(x, np.hstack(to_remove))
|
||||
# else:
|
||||
# return x
|
||||
#
|
||||
# def _set_params_transformed(self, x):
|
||||
# """ takes the vector x, which is then modified (by untying, reparameterising or inserting fixed values), and then call self._set_params"""
|
||||
# self._set_params(self._untransform_params(x))
|
||||
#
|
||||
# def _untransform_params(self, x):
|
||||
# """
|
||||
# The Transformation required for _set_params_transformed.
|
||||
#
|
||||
# This moves the vector x seen by the optimiser (unconstrained) to the
|
||||
# valid param vector seen by the model
|
||||
#
|
||||
# Note:
|
||||
# - This function is separate from _set_params_transformed for downstream flexibility
|
||||
# """
|
||||
# # work out how many places are fixed, and where they are. tricky logic!
|
||||
# fix_places = self.fixed_indices + [t[1:] for t in self.tied_indices]
|
||||
# if len(fix_places):
|
||||
# fix_places = np.hstack(fix_places)
|
||||
# Nfix_places = fix_places.size
|
||||
# else:
|
||||
# Nfix_places = 0
|
||||
#
|
||||
# free_places = np.setdiff1d(np.arange(Nfix_places + x.size, dtype=np.int), fix_places)
|
||||
#
|
||||
# # put the models values in the vector xx
|
||||
# xx = np.zeros(Nfix_places + free_places.size, dtype=np.float64)
|
||||
#
|
||||
# xx[free_places] = x
|
||||
# [np.put(xx, i, v) for i, v in zip(self.fixed_indices, self.fixed_values)]
|
||||
# [np.put(xx, i, v) for i, v in [(t[1:], xx[t[0]]) for t in self.tied_indices] ]
|
||||
#
|
||||
# [np.put(xx, i, t.f(xx[i])) for i, t in zip(self.constrained_indices, self.constraints)]
|
||||
# if hasattr(self, 'debug'):
|
||||
# stop # @UndefinedVariable
|
||||
#
|
||||
# return xx
|
||||
#
|
||||
# def _get_param_names_transformed(self):
|
||||
# """
|
||||
# Returns the param parameter_names as propagated after constraining,
|
||||
# tying or fixing, i.e. a list of the same length as _get_params_transformed()
|
||||
# """
|
||||
# n = self._get_param_names()
|
||||
#
|
||||
# # remove/concatenate the tied param parameter_names
|
||||
# if len(self.tied_indices):
|
||||
# for t in self.tied_indices:
|
||||
# n[t[0]] = "<tie>".join([n[tt] for tt in t])
|
||||
# remove = np.hstack([t[1:] for t in self.tied_indices])
|
||||
# else:
|
||||
# remove = np.empty(shape=(0,), dtype=np.int)
|
||||
#
|
||||
# # also remove the fixed params
|
||||
# if len(self.fixed_indices):
|
||||
# remove = np.hstack((remove, np.hstack(self.fixed_indices)))
|
||||
#
|
||||
# # add markers to show that some variables are constrained
|
||||
# for i, t in zip(self.constrained_indices, self.constraints):
|
||||
# for ii in i:
|
||||
# n[ii] = n[ii] + t.__str__()
|
||||
#
|
||||
# n = [nn for i, nn in enumerate(n) if not i in remove]
|
||||
# return n
|
||||
#
|
||||
# #@property
|
||||
# #def all(self):
|
||||
# # return self.__str__(self._get_param_names())
|
||||
#
|
||||
#
|
||||
# #def __str__(self, parameter_names=None, nw=30):
|
||||
# def __str__(self, nw=30):
|
||||
# """
|
||||
# Return a string describing the param parameter_names and their ties and constraints
|
||||
# """
|
||||
# parameter_names = self._get_param_names()
|
||||
# #if parameter_names is None:
|
||||
# # parameter_names = self._get_print_names()
|
||||
# #name_indices = self.grep_param_names("|".join(parameter_names))
|
||||
# N = len(parameter_names)
|
||||
#
|
||||
# if not N:
|
||||
# return "This object has no free parameters."
|
||||
# header = ['Name', 'Value', 'Constraints', 'Ties']
|
||||
# values = self._get_params() # map(str,self._get_params())
|
||||
# #values = self._get_params()[name_indices] # map(str,self._get_params())
|
||||
# # sort out the constraints
|
||||
# constraints = [''] * len(parameter_names)
|
||||
# #constraints = [''] * len(self._get_param_names())
|
||||
# for i, t in zip(self.constrained_indices, self.constraints):
|
||||
# for ii in i:
|
||||
# constraints[ii] = t.__str__()
|
||||
# for i in self.fixed_indices:
|
||||
# for ii in i:
|
||||
# constraints[ii] = 'Fixed'
|
||||
# # sort out the ties
|
||||
# ties = [''] * len(parameter_names)
|
||||
# for i, tie in enumerate(self.tied_indices):
|
||||
# for j in tie:
|
||||
# ties[j] = '(' + str(i) + ')'
|
||||
#
|
||||
# if values.size == 1:
|
||||
# values = ['%.4f' %float(values)]
|
||||
# else:
|
||||
# values = ['%.4f' % float(v) for v in values]
|
||||
# max_names = max([len(parameter_names[i]) for i in range(len(parameter_names))] + [len(header[0])])
|
||||
# max_values = max([len(values[i]) for i in range(len(values))] + [len(header[1])])
|
||||
# max_constraint = max([len(constraints[i]) for i in range(len(constraints))] + [len(header[2])])
|
||||
# max_ties = max([len(ties[i]) for i in range(len(ties))] + [len(header[3])])
|
||||
# cols = np.array([max_names, max_values, max_constraint, max_ties]) + 4
|
||||
# # columns = cols.sum()
|
||||
#
|
||||
# header_string = ["{h:^{col}}".format(h=header[i], col=cols[i]) for i in range(len(cols))]
|
||||
# header_string = map(lambda x: '|'.join(x), [header_string])
|
||||
# separator = '-' * len(header_string[0])
|
||||
# param_string = ["{n:^{c0}}|{v:^{c1}}|{c:^{c2}}|{t:^{c3}}".format(n=parameter_names[i], v=values[i], c=constraints[i], t=ties[i], c0=cols[0], c1=cols[1], c2=cols[2], c3=cols[3]) for i in range(len(values))]
|
||||
#
|
||||
#
|
||||
# return ('\n'.join([header_string[0], separator] + param_string)) + '\n'
|
||||
#
|
||||
# def grep_model(self,regexp):
|
||||
# regexp_indices = self.grep_param_names(regexp)
|
||||
# all_names = self._get_param_names()
|
||||
#
|
||||
# parameter_names = [all_names[pj] for pj in regexp_indices]
|
||||
# N = len(parameter_names)
|
||||
#
|
||||
# if not N:
|
||||
# return "Match not found."
|
||||
#
|
||||
# header = ['Name', 'Value', 'Constraints', 'Ties']
|
||||
# all_values = self._get_params()
|
||||
# values = np.array([all_values[pj] for pj in regexp_indices])
|
||||
# constraints = [''] * len(parameter_names)
|
||||
#
|
||||
# _constrained_indices,aux = self._pick_elements(regexp_indices,self.constrained_indices)
|
||||
# _constraints_ = [self.constraints[pj] for pj in aux]
|
||||
#
|
||||
# for i, t in zip(_constrained_indices, _constraints_):
|
||||
# for ii in i:
|
||||
# iii = regexp_indices.tolist().index(ii)
|
||||
# constraints[iii] = t.__str__()
|
||||
#
|
||||
# _fixed_indices,aux = self._pick_elements(regexp_indices,self.fixed_indices)
|
||||
# for i in _fixed_indices:
|
||||
# for ii in i:
|
||||
# iii = regexp_indices.tolist().index(ii)
|
||||
# constraints[ii] = 'Fixed'
|
||||
#
|
||||
# _tied_indices,aux = self._pick_elements(regexp_indices,self.tied_indices)
|
||||
# ties = [''] * len(parameter_names)
|
||||
# for i,ti in zip(_tied_indices,aux):
|
||||
# for ii in i:
|
||||
# iii = regexp_indices.tolist().index(ii)
|
||||
# ties[iii] = '(' + str(ti) + ')'
|
||||
#
|
||||
# if values.size == 1:
|
||||
# values = ['%.4f' %float(values)]
|
||||
# else:
|
||||
# values = ['%.4f' % float(v) for v in values]
|
||||
#
|
||||
# max_names = max([len(parameter_names[i]) for i in range(len(parameter_names))] + [len(header[0])])
|
||||
# max_values = max([len(values[i]) for i in range(len(values))] + [len(header[1])])
|
||||
# max_constraint = max([len(constraints[i]) for i in range(len(constraints))] + [len(header[2])])
|
||||
# max_ties = max([len(ties[i]) for i in range(len(ties))] + [len(header[3])])
|
||||
# cols = np.array([max_names, max_values, max_constraint, max_ties]) + 4
|
||||
#
|
||||
# header_string = ["{h:^{col}}".format(h=header[i], col=cols[i]) for i in range(len(cols))]
|
||||
# header_string = map(lambda x: '|'.join(x), [header_string])
|
||||
# separator = '-' * len(header_string[0])
|
||||
# param_string = ["{n:^{c0}}|{v:^{c1}}|{c:^{c2}}|{t:^{c3}}".format(n=parameter_names[i], v=values[i], c=constraints[i], t=ties[i], c0=cols[0], c1=cols[1], c2=cols[2], c3=cols[3]) for i in range(len(values))]
|
||||
#
|
||||
# print header_string[0]
|
||||
# print separator
|
||||
# for string in param_string:
|
||||
# print string
|
||||
#
|
||||
# def _pick_elements(self,regexp_ind,array_list):
|
||||
# """Removes from array_list the elements different from regexp_ind"""
|
||||
# new_array_list = [] #New list with elements matching regexp_ind
|
||||
# array_indices = [] #Indices that matches the arrays in new_array_list and array_list
|
||||
#
|
||||
# array_index = 0
|
||||
# for array in array_list:
|
||||
# _new = []
|
||||
# for ai in array:
|
||||
# if ai in regexp_ind:
|
||||
# _new.append(ai)
|
||||
# if len(_new):
|
||||
# new_array_list.append(np.array(_new))
|
||||
# array_indices.append(array_index)
|
||||
# array_index += 1
|
||||
# return new_array_list, array_indices
|
||||
|
||||
|
|
|
|||
|
|
@ -22,15 +22,15 @@ class ExactGaussianInference(object):
|
|||
|
||||
def get_YYTfactor(self, Y):
|
||||
"""
|
||||
find a matrix L which satisfies LLT = YYT.
|
||||
find a matrix L which satisfies LL^T = YY^T.
|
||||
|
||||
Note that L may have fewer columns than Y.
|
||||
Note that L may have fewer columns than Y, else L=Y.
|
||||
"""
|
||||
N, D = Y.shape
|
||||
if (N>D):
|
||||
return Y
|
||||
else:
|
||||
#if Y in self.cache, return self.Cache[Y], else stor Y in cache and return L.
|
||||
#if Y in self.cache, return self.Cache[Y], else store Y in cache and return L.
|
||||
raise NotImplementedError, 'TODO' #TODO
|
||||
|
||||
def inference(self, K, likelihood, Y, Y_metadata=None):
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue