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merge chagnes from devel

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Zhenwen Dai 2014-05-15 18:02:33 +01:00
commit 4ec8f464e2
64 changed files with 2592 additions and 10697 deletions
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20
GPy/core/gp.py
View file

@ -10,7 +10,7 @@ from model import Model
from parameterization import ObsAr
from .. import likelihoods
from ..likelihoods.gaussian import Gaussian
from ..inference.latent_function_inference import exact_gaussian_inference, expectation_propagation
from ..inference.latent_function_inference import exact_gaussian_inference, expectation_propagation, LatentFunctionInference
from parameterization.variational import VariationalPosterior
class GP(Model):
@ -21,6 +21,7 @@ class GP(Model):
:param Y: output observations
:param kernel: a GPy kernel, defaults to rbf+white
:param likelihood: a GPy likelihood
:param :class:`~GPy.inference.latent_function_inference.LatentFunctionInference` inference_method: The inference method to use for this GP
:rtype: model object
.. Note:: Multiple independent outputs are allowed using columns of Y
@ -220,3 +221,20 @@ class GP(Model):
"""
return self.kern.input_sensitivity()
def optimize(self, optimizer=None, start=None, **kwargs):
"""
Optimize the model using self.log_likelihood and self.log_likelihood_gradient, as well as self.priors.
kwargs are passed to the optimizer. They can be:
:param max_f_eval: maximum number of function evaluations
:type max_f_eval: int
:messages: whether to display during optimisation
:type messages: bool
:param optimizer: which optimizer to use (defaults to self.preferred optimizer)
:type optimizer: string
TODO: valid args
"""
self.inference_method.on_optimization_start()
super(GP, self).optimize(optimizer, start, **kwargs)
self.inference_method.on_optimization_end()

2
GPy/core/model.py
View file

@ -220,7 +220,7 @@ class Model(Parameterized):
if self.is_fixed:
raise RuntimeError, "Cannot optimize, when everything is fixed"
if self.size == 0:
raise RuntimeError, "Model without parameters cannot be minimized"
raise RuntimeError, "Model without parameters cannot be optimized"
if optimizer is None:
optimizer = self.preferred_optimizer

47
GPy/core/parameterization/lists_and_dicts.py
View file

@ -38,7 +38,12 @@ class ArrayList(list):
raise ValueError, "{} is not in list".format(item)
pass
class ObservablesList(object):
class ObserverList(object):
"""
A list which containts the observables.
It only holds weak references to observers, such that unbound
observers dont dangle in memory.
"""
def __init__(self):
self._poc = []
@ -46,26 +51,31 @@ class ObservablesList(object):
p,o,c = self._poc[ind]
return p, o(), c
def remove(self, priority, observable, callble):
def remove(self, priority, observer, callble):
"""
Remove one observer, which had priority and callble.
"""
self.flush()
for i in range(len(self) - 1, -1, -1):
p,o,c = self[i]
if priority==p and observable==o and callble==c:
if priority==p and observer==o and callble==c:
del self._poc[i]
def __repr__(self):
return self._poc.__repr__()
def add(self, priority, observable, callble):
ins = 0
for pr, _, _ in self:
if priority > pr:
break
ins += 1
self._poc.insert(ins, (priority, weakref.ref(observable), callble))
def add(self, priority, observer, callble):
"""
Add an observer with priority and callble
"""
if observer is not None:
ins = 0
for pr, _, _ in self:
if priority > pr:
break
ins += 1
self._poc.insert(ins, (priority, weakref.ref(observer), callble))
def __str__(self):
ret = []
curr_p = None
@ -81,23 +91,26 @@ class ObservablesList(object):
return '\n'.join(ret)
def flush(self):
"""
Make sure all weak references, which point to nothing are flushed (deleted)
"""
self._poc = [(p,o,c) for p,o,c in self._poc if o() is not None]
def __iter__(self):
self.flush()
for p, o, c in self._poc:
if o() is not None:
yield p, o(), c
yield p, o(), c
def __len__(self):
self.flush()
return self._poc.__len__()
def __deepcopy__(self, memo):
self.flush()
s = ObservablesList()
import copy
s._poc = copy.deepcopy(self._poc, memo)
s = ObserverList()
for p,o,c in self:
import copy
s.add(p, copy.deepcopy(o, memo), copy.deepcopy(c, memo))
s.flush()
return s
def __getstate__(self):

6
GPy/core/parameterization/observable_array.py
View file

@ -1,7 +1,7 @@
# Copyright (c) 2012, GPy authors (see AUTHORS.txt).
# Licensed under the BSD 3-clause license (see LICENSE.txt)
__updated__ = '2014-04-15'
__updated__ = '2014-05-12'
import numpy as np
from parameter_core import Observable, Pickleable
@ -15,10 +15,10 @@ class ObsAr(np.ndarray, Pickleable, Observable):
"""
__array_priority__ = -1 # Never give back ObsAr
def __new__(cls, input_array, *a, **kw):
# allways make a copy of input paramters, as we need it to be in C order:
if not isinstance(input_array, ObsAr):
obj = np.atleast_1d(np.require(input_array, dtype=np.float64, requirements=['W', 'C'])).view(cls)
obj = np.atleast_1d(np.require(np.copy(input_array), dtype=np.float64, requirements=['W', 'C'])).view(cls)
else: obj = input_array
#cls.__name__ = "ObsAr" # because of fixed printing of `array` in np printing
super(ObsAr, obj).__init__(*a, **kw)
return obj

86
GPy/core/parameterization/param.py
View file

@ -4,7 +4,7 @@
import itertools
import numpy
np = numpy
from parameter_core import OptimizationHandlable, adjust_name_for_printing
from parameter_core import Parameterizable, adjust_name_for_printing
from observable_array import ObsAr
###### printing
@ -16,7 +16,7 @@ __precision__ = numpy.get_printoptions()['precision'] # numpy printing precision
__print_threshold__ = 5
######
class Param(OptimizationHandlable, ObsAr):
class Param(Parameterizable, ObsAr):
"""
Parameter object for GPy models.
@ -42,10 +42,9 @@ class Param(OptimizationHandlable, ObsAr):
"""
__array_priority__ = -1 # Never give back Param
_fixes_ = None
_parameters_ = []
parameters = []
def __new__(cls, name, input_array, default_constraint=None):
obj = numpy.atleast_1d(super(Param, cls).__new__(cls, input_array=input_array))
cls.__name__ = "Param"
obj._current_slice_ = (slice(obj.shape[0]),)
obj._realshape_ = obj.shape
obj._realsize_ = obj.size
@ -58,9 +57,9 @@ class Param(OptimizationHandlable, ObsAr):
def build_pydot(self,G):
import pydot
node = pydot.Node(id(self), shape='record', label=self.name)
node = pydot.Node(id(self), shape='trapezium', label=self.name)#, fontcolor='white', color='white')
G.add_node(node)
for o in self.observers.keys():
for _, o, _ in self.observers:
label = o.name if hasattr(o, 'name') else str(o)
observed_node = pydot.Node(id(o), label=label)
G.add_node(observed_node)
@ -88,8 +87,18 @@ class Param(OptimizationHandlable, ObsAr):
@property
def param_array(self):
"""
As we are a leaf, this just returns self
"""
return self
@property
def values(self):
"""
Return self as numpy array view
"""
return self.view(np.ndarray)
@property
def gradient(self):
"""
@ -100,11 +109,11 @@ class Param(OptimizationHandlable, ObsAr):
"""
if getattr(self, '_gradient_array_', None) is None:
self._gradient_array_ = numpy.empty(self._realshape_, dtype=numpy.float64)
return self._gradient_array_[self._current_slice_]
return self._gradient_array_#[self._current_slice_]
@gradient.setter
def gradient(self, val):
self._gradient_array_[self._current_slice_] = val
self._gradient_array_[:] = val
#===========================================================================
# Array operations -> done
@ -112,10 +121,13 @@ class Param(OptimizationHandlable, ObsAr):
def __getitem__(self, s, *args, **kwargs):
if not isinstance(s, tuple):
s = (s,)
if not reduce(lambda a, b: a or numpy.any(b is Ellipsis), s, False) and len(s) <= self.ndim:
s += (Ellipsis,)
#if not reduce(lambda a, b: a or numpy.any(b is Ellipsis), s, False) and len(s) <= self.ndim:
# s += (Ellipsis,)
new_arr = super(Param, self).__getitem__(s, *args, **kwargs)
try: new_arr._current_slice_ = s; new_arr._original_ = self.base is new_arr.base
try:
new_arr._current_slice_ = s
new_arr._gradient_array_ = self.gradient[s]
new_arr._original_ = self.base is new_arr.base
except AttributeError: pass # returning 0d array or float, double etc
return new_arr
@ -130,6 +142,9 @@ class Param(OptimizationHandlable, ObsAr):
def _raveled_index_for(self, obj):
return self._raveled_index()
#===========================================================================
# Index recreation
#===========================================================================
def _expand_index(self, slice_index=None):
# this calculates the full indexing arrays from the slicing objects given by get_item for _real..._ attributes
# it basically translates slices to their respective index arrays and turns negative indices around
@ -138,6 +153,8 @@ class Param(OptimizationHandlable, ObsAr):
slice_index = self._current_slice_
def f(a):
a, b = a
if isinstance(a, numpy.ndarray) and a.dtype == bool:
raise ValueError, "Boolean indexing not implemented, use Param[np.where(index)] to index by boolean arrays!"
if a not in (slice(None), Ellipsis):
if isinstance(a, slice):
start, stop, step = a.indices(b)
@ -156,6 +173,36 @@ class Param(OptimizationHandlable, ObsAr):
def _ensure_fixes(self):
if not self._has_fixes(): self._fixes_ = numpy.ones(self._realsize_, dtype=bool)
#===========================================================================
# parameterizable
#===========================================================================
def traverse(self, visit, *args, **kwargs):
"""
Traverse the hierarchy performing visit(self, *args, **kwargs) at every node passed by.
See "visitor pattern" in literature. This is implemented in pre-order fashion.
This will function will just call visit on self, as Param are leaf nodes.
"""
self.__visited = True
visit(self, *args, **kwargs)
self.__visited = False
def traverse_parents(self, visit, *args, **kwargs):
"""
Traverse the hierarchy upwards, visiting all parents and their children, except self.
See "visitor pattern" in literature. This is implemented in pre-order fashion.
Example:
parents = []
self.traverse_parents(parents.append)
print parents
"""
if self.has_parent():
self.__visited = True
self._parent_._traverse_parents(visit, *args, **kwargs)
self.__visited = False
#===========================================================================
# Convenience
#===========================================================================
@ -228,9 +275,16 @@ class Param(OptimizationHandlable, ObsAr):
and len(set(map(len, clean_curr_slice))) <= 1):
return numpy.fromiter(itertools.izip(*clean_curr_slice),
dtype=[('', int)] * self._realndim_, count=len(clean_curr_slice[0])).view((int, self._realndim_))
expanded_index = list(self._expand_index(slice_index))
return numpy.fromiter(itertools.product(*expanded_index),
try:
expanded_index = list(self._expand_index(slice_index))
indices = numpy.fromiter(itertools.product(*expanded_index),
dtype=[('', int)] * self._realndim_, count=reduce(lambda a, b: a * b.size, expanded_index, 1)).view((int, self._realndim_))
except:
print "Warning: extended indexing was used"
indices = np.indices(self._realshape_, dtype=int)
indices = indices[(slice(None),)+slice_index]
indices = np.rollaxis(indices, 0, indices.ndim)
return indices
def _max_len_names(self, gen, header):
gen = map(lambda x: " ".join(map(str, x)), gen)
return reduce(lambda a, b:max(a, len(b)), gen, len(header))
@ -272,7 +326,7 @@ class Param(OptimizationHandlable, ObsAr):
class ParamConcatenation(object):
def __init__(self, params):
"""
Parameter concatenation for convienience of printing regular expression matched arrays
Parameter concatenation for convenience of printing regular expression matched arrays
you can index this concatenation as if it was the flattened concatenation
of all the parameters it contains, same for setting parameters (Broadcasting enabled).
@ -316,8 +370,8 @@ class ParamConcatenation(object):
val = val.values()
ind = numpy.zeros(sum(self._param_sizes), dtype=bool); ind[s] = True;
vals = self.values(); vals[s] = val
[numpy.copyto(p, vals[ps], where=ind[ps])
for p, ps in zip(self.params, self._param_slices_)]
for p, ps in zip(self.params, self._param_slices_):
p.flat[ind[ps]] = vals[ps]
if update:
self.update_all_params()
def values(self):

458
GPy/core/parameterization/parameter_core.py
View file

@ -17,7 +17,7 @@ from transformations import Logexp, NegativeLogexp, Logistic, __fixed__, FIXED,
import numpy as np
import re
__updated__ = '2014-04-16'
__updated__ = '2014-05-15'
class HierarchyError(Exception):
"""
@ -52,13 +52,23 @@ class Observable(object):
_updated = True
def __init__(self, *args, **kwargs):
super(Observable, self).__init__()
from lists_and_dicts import ObservablesList
self.observers = ObservablesList()
from lists_and_dicts import ObserverList
self.observers = ObserverList()
def add_observer(self, observer, callble, priority=0):
"""
Add an observer `observer` with the callback `callble`
and priority `priority` to this observers list.
"""
self.observers.add(priority, observer, callble)
def remove_observer(self, observer, callble=None):
"""
Either (if callble is None) remove all callables,
which were added alongside observer,
or remove callable `callble` which was added alongside
the observer `observer`.
"""
to_remove = []
for poc in self.observers:
_, obs, clble = poc
@ -91,10 +101,6 @@ class Observable(object):
break
callble(self, which=which)
#===============================================================================
# Foundation framework for parameterized and param objects:
#===============================================================================
class Parentable(object):
"""
Enable an Object to have a parent.
@ -124,7 +130,7 @@ class Parentable(object):
"""
Disconnect this object from its parent
"""
raise NotImplementedError, "Abstaract superclass"
raise NotImplementedError, "Abstract superclass"
@property
def _highest_parent_(self):
@ -162,7 +168,6 @@ class Pickleable(object):
:param protocol: pickling protocol to use, python-pickle for details.
"""
import cPickle as pickle
import pickle #TODO: cPickle
if isinstance(f, str):
with open(f, 'w') as f:
pickle.dump(self, f, protocol)
@ -173,38 +178,42 @@ class Pickleable(object):
# copy and pickling
#===========================================================================
def copy(self):
"""Returns a (deep) copy of the current model"""
"""
Returns a (deep) copy of the current parameter handle.
All connections to parents of the copy will be cut.
"""
#raise NotImplementedError, "Copy is not yet implemented, TODO: Observable hierarchy"
import copy
memo = {}
memo[id(self._parent_)] = None
memo[id(self.gradient)] = None
memo[id(self.param_array)] = None
memo[id(self._fixes_)] = None
c = copy.deepcopy(self, memo)
# the next part makes sure that we do not include parents in any form:
parents = []
self.traverse_parents(parents.append) # collect parents
for p in parents:
memo[id(p)] = None # set all parents to be None, so they will not be copied
memo[id(self.gradient)] = None # reset the gradient
memo[id(self.param_array)] = None # and param_array
memo[id(self._fixes_)] = None # fixes have to be reset, as this is now highest parent
c = copy.deepcopy(self, memo) # and start the copy
c._parent_index_ = None
return c
def __deepcopy__(self, memo):
s = self.__new__(self.__class__)
memo[id(self)] = s
s = self.__new__(self.__class__) # fresh instance
memo[id(self)] = s # be sure to break all cycles --> self is already done
import copy
s.__dict__.update(copy.deepcopy(self.__dict__, memo))
s.__dict__.update(copy.deepcopy(self.__dict__, memo)) # standard copy
return s
def __getstate__(self):
ignore_list = ([#'_parent_', '_parent_index_',
#'observers',
'_param_array_', '_gradient_array_', '_fixes_',
'_Cacher_wrap__cachers']
#+ self.parameter_names(recursive=False)
)
ignore_list = ['_param_array_', # parameters get set from bottom to top
'_gradient_array_', # as well as gradients
'_fixes_', # and fixes
'_Cacher_wrap__cachers', # never pickle cachers
]
dc = dict()
for k,v in self.__dict__.iteritems():
if k not in ignore_list:
#if hasattr(v, "__getstate__"):
#dc[k] = v.__getstate__()
#else:
dc[k] = v
return dc
@ -212,12 +221,6 @@ class Pickleable(object):
self.__dict__.update(state)
return self
#def __getstate__(self, memo):
# raise NotImplementedError, "get state must be implemented to be able to pickle objects"
#def __setstate__(self, memo):
# raise NotImplementedError, "set state must be implemented to be able to pickle objects"
class Gradcheckable(Pickleable, Parentable):
"""
Adds the functionality for an object to be gradcheckable.
@ -252,7 +255,6 @@ class Gradcheckable(Pickleable, Parentable):
"""
raise HierarchyError, "This parameter is not in a model with a likelihood, and, therefore, cannot be gradient checked!"
class Nameable(Gradcheckable):
"""
Make an object nameable inside the hierarchy.
@ -291,41 +293,8 @@ class Nameable(Gradcheckable):
return self._parent_.hierarchy_name() + "." + adjust(self.name)
return adjust(self.name)
class Indexable(object):
"""
Enable enraveled indexes and offsets for this object.
The raveled index of an object is the index for its parameters in a flattened int array.
"""
def __init__(self, *a, **kw):
super(Indexable, self).__init__()
def _raveled_index(self):
"""
Flattened array of ints, specifying the index of this object.
This has to account for shaped parameters!
"""
raise NotImplementedError, "Need to be able to get the raveled Index"
def _offset_for(self, param):
"""
Return the offset of the param inside this parameterized object.
This does not need to account for shaped parameters, as it
basically just sums up the parameter sizes which come before param.
"""
return 0
#raise NotImplementedError, "shouldnt happen, offset required from non parameterization object?"
def _raveled_index_for(self, param):
"""
get the raveled index for a param
that is an int array, containing the indexes for the flattened
param inside this parameterized logic.
"""
return param._raveled_index()
#raise NotImplementedError, "shouldnt happen, raveld index transformation required from non parameterization object?"
class Constrainable(Nameable, Indexable, Observable):
class Indexable(Nameable, Observable):
"""
Make an object constrainable with Priors and Transformations.
TODO: Mappings!!
@ -336,7 +305,7 @@ class Constrainable(Nameable, Indexable, Observable):
:func:`constrain()` and :func:`unconstrain()` are main methods here
"""
def __init__(self, name, default_constraint=None, *a, **kw):
super(Constrainable, self).__init__(name=name, *a, **kw)
super(Indexable, self).__init__(name=name, *a, **kw)
self._default_constraint_ = default_constraint
from index_operations import ParameterIndexOperations
self.constraints = ParameterIndexOperations()
@ -358,6 +327,39 @@ class Constrainable(Nameable, Indexable, Observable):
self._connect_fixes()
self._notify_parent_change()
#===========================================================================
# Indexable
#===========================================================================
def _offset_for(self, param):
"""
Return the offset of the param inside this parameterized object.
This does not need to account for shaped parameters, as it
basically just sums up the parameter sizes which come before param.
"""
if param.has_parent():
if param._parent_._get_original(param) in self.parameters:
return self._param_slices_[param._parent_._get_original(param)._parent_index_].start
return self._offset_for(param._parent_) + param._parent_._offset_for(param)
return 0
def _raveled_index_for(self, param):
"""
get the raveled index for a param
that is an int array, containing the indexes for the flattened
param inside this parameterized logic.
"""
from param import ParamConcatenation
if isinstance(param, ParamConcatenation):
return np.hstack((self._raveled_index_for(p) for p in param.params))
return param._raveled_index() + self._offset_for(param)
def _raveled_index(self):
"""
Flattened array of ints, specifying the index of this object.
This has to account for shaped parameters!
"""
return np.r_[:self.size]
#===========================================================================
# Fixing Parameters:
#===========================================================================
@ -412,9 +414,24 @@ class Constrainable(Nameable, Indexable, Observable):
self._fixes_ = None
del self.constraints[__fixed__]
#===========================================================================
# Convenience for fixed
#===========================================================================
def _has_fixes(self):
return hasattr(self, "_fixes_") and self._fixes_ is not None and self._fixes_.size == self.size
@property
def is_fixed(self):
for p in self.parameters:
if not p.is_fixed: return False
return True
def _get_original(self, param):
# if advanced indexing is activated it happens that the array is a copy
# you can retrieve the original param through this method, by passing
# the copy here
return self.parameters[param._parent_index_]
#===========================================================================
# Prior Operations
#===========================================================================
@ -438,8 +455,7 @@ class Constrainable(Nameable, Indexable, Observable):
def unset_priors(self, *priors):
"""
Un-set all priors given from this parameter handle.
Un-set all priors given (in *priors) from this parameter handle.
"""
return self._remove_from_index_operations(self.priors, priors)
@ -541,13 +557,13 @@ class Constrainable(Nameable, Indexable, Observable):
self.constraints = ParameterIndexOperationsView(parent.constraints, parent._offset_for(self), self.size)
self.priors = ParameterIndexOperationsView(parent.priors, parent._offset_for(self), self.size)
self._fixes_ = None
for p in self._parameters_:
for p in self.parameters:
p._parent_changed(parent)
def _add_to_index_operations(self, which, reconstrained, what, warning):
"""
Helper preventing copy code.
This addes the given what (transformation, prior etc) to parameter index operations which.
This adds the given what (transformation, prior etc) to parameter index operations which.
revonstrained are reconstrained indices.
warn when reconstraining parameters if warning is True.
TODO: find out which parameters have changed specifically
@ -569,14 +585,13 @@ class Constrainable(Nameable, Indexable, Observable):
removed = np.empty((0,), dtype=int)
for t in transforms:
unconstrained = which.remove(t, self._raveled_index())
print unconstrained
removed = np.union1d(removed, unconstrained)
if t is __fixed__:
self._highest_parent_._set_unfixed(self, unconstrained)
return removed
class OptimizationHandlable(Constrainable):
class OptimizationHandlable(Indexable):
"""
This enables optimization handles on an Object as done in GPy 0.4.
@ -585,14 +600,8 @@ class OptimizationHandlable(Constrainable):
def __init__(self, name, default_constraint=None, *a, **kw):
super(OptimizationHandlable, self).__init__(name, default_constraint=default_constraint, *a, **kw)
def transform(self):
[np.put(self.param_array, ind, c.finv(self.param_array.flat[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__]
def untransform(self):
[np.put(self.param_array, ind, c.f(self.param_array.flat[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__]
def _get_params_transformed(self):
# transformed parameters (apply transformation rules)
# transformed parameters (apply un-transformation rules)
p = self.param_array.copy()
[np.put(p, ind, c.finv(p[ind])) for c, ind in self.constraints.iteritems() if c != __fixed__]
if self.has_parent() and self.constraints[__fixed__].size != 0:
@ -604,29 +613,55 @@ class OptimizationHandlable(Constrainable):
return p
def _set_params_transformed(self, p):
if p is self.param_array:
p = p.copy()
if self.has_parent() and self.constraints[__fixed__].size != 0:
fixes = np.ones(self.size).astype(bool)
fixes[self.constraints[__fixed__]] = FIXED
self.param_array.flat[fixes] = p
elif self._has_fixes(): self.param_array.flat[self._fixes_] = p
else: self.param_array.flat = p
self.untransform()
"""
Set parameters p, but make sure they get transformed before setting.
This means, the optimizer sees p, whereas the model sees transformed(p),
such that, the parameters the model sees are in the right domain.
"""
if not(p is self.param_array):
if self.has_parent() and self.constraints[__fixed__].size != 0:
fixes = np.ones(self.size).astype(bool)
fixes[self.constraints[__fixed__]] = FIXED
self.param_array.flat[fixes] = p
elif self._has_fixes(): self.param_array.flat[self._fixes_] = p
else: self.param_array.flat = p
[np.put(self.param_array, ind, c.f(self.param_array.flat[ind]))
for c, ind in self.constraints.iteritems() if c != __fixed__]
self._trigger_params_changed()
def _trigger_params_changed(self, trigger_parent=True):
[p._trigger_params_changed(trigger_parent=False) for p in self._parameters_]
"""
First tell all children to update,
then update yourself.
If trigger_parent is True, we will tell the parent, otherwise not.
"""
[p._trigger_params_changed(trigger_parent=False) for p in self.parameters]
self.notify_observers(None, None if trigger_parent else -np.inf)
def _size_transformed(self):
"""
As fixes are not passed to the optimiser, the size of the model for the optimiser
is the size of all parameters minus the size of the fixes.
"""
return self.size - self.constraints[__fixed__].size
def _transform_gradients(self, g):
"""
Transform the gradients by multiplying the gradient factor for each
constraint to it.
"""
if self.has_parent():
return g
[np.put(g, i, g[i] * c.gradfactor(self.param_array[i])) for c, i in self.constraints.iteritems() if c != __fixed__]
if self._has_fixes(): return g[self._fixes_]
return g
@property
def num_params(self):
"""
Return the number of parameters of this parameter_handle.
Param objects will allways return 0.
Param objects will always return 0.
"""
raise NotImplemented, "Abstract, please implement in respective classes"
@ -640,10 +675,11 @@ class OptimizationHandlable(Constrainable):
"""
if adjust_for_printing: adjust = lambda x: adjust_name_for_printing(x)
else: adjust = lambda x: x
if recursive: names = [xi for x in self._parameters_ for xi in x.parameter_names(add_self=True, adjust_for_printing=adjust_for_printing)]
else: names = [adjust(x.name) for x in self._parameters_]
if recursive: names = [xi for x in self.parameters for xi in x.parameter_names(add_self=True, adjust_for_printing=adjust_for_printing)]
else: names = [adjust(x.name) for x in self.parameters]
if add_self: names = map(lambda x: adjust(self.name) + "." + x, names)
return names
def _get_param_names(self):
n = np.array([p.hierarchy_name() + '[' + str(i) + ']' for p in self.flattened_parameters for i in p._indices()])
return n
@ -662,7 +698,7 @@ class OptimizationHandlable(Constrainable):
Randomize the model.
Make this draw from the prior if one exists, else draw from given random generator
:param rand_gen: numpy random number generator which takes args and kwargs
:param rand_gen: np random number generator which takes args and kwargs
:param flaot loc: loc parameter for random number generator
:param float scale: scale parameter for random number generator
:param args, kwargs: will be passed through to random number generator
@ -678,7 +714,7 @@ class OptimizationHandlable(Constrainable):
# for all parameterized objects
#===========================================================================
@property
def full_gradient(self):
def gradient_full(self):
"""
Note to users:
This does not return the gradient in the right shape! Use self.gradient
@ -692,30 +728,53 @@ class OptimizationHandlable(Constrainable):
return self._gradient_array_
def _propagate_param_grad(self, parray, garray):
"""
For propagating the param_array and gradient_array.
This ensures the in memory view of each subsequent array.
1.) connect param_array of children to self.param_array
2.) tell all children to propagate further
"""
pi_old_size = 0
for pi in self._parameters_:
for pi in self.parameters:
pislice = slice(pi_old_size, pi_old_size + pi.size)
self.param_array[pislice] = pi.param_array.flat # , requirements=['C', 'W']).flat
self.full_gradient[pislice] = pi.full_gradient.flat # , requirements=['C', 'W']).flat
self.gradient_full[pislice] = pi.gradient_full.flat # , requirements=['C', 'W']).flat
pi.param_array.data = parray[pislice].data
pi.full_gradient.data = garray[pislice].data
pi.gradient_full.data = garray[pislice].data
pi._propagate_param_grad(parray[pislice], garray[pislice])
pi_old_size += pi.size
class Parameterizable(OptimizationHandlable):
"""
A parameterisable class.
This class provides the parameters list (ArrayList) and standard parameter handling,
such as {add|remove}_parameter(), traverse hierarchy and param_array, gradient_array
and the empty parameters_changed().
This class is abstract and should not be instantiated.
Use GPy.core.Parameterized() as node (or leaf) in the parameterized hierarchy.
Use GPy.core.Param() for a leaf in the parameterized hierarchy.
"""
def __init__(self, *args, **kwargs):
super(Parameterizable, self).__init__(*args, **kwargs)
from GPy.core.parameterization.lists_and_dicts import ArrayList
self._parameters_ = ArrayList()
self.size = 0
self.parameters = ArrayList()
self._param_array_ = None
self._added_names_ = set()
self.__visited = False # for traversing in reverse order we need to know if we were here already
@property
def param_array(self):
if not hasattr(self, '_param_array_'):
"""
Array representing the parameters of this class.
There is only one copy of all parameters in memory, two during optimization.
"""
if self.__dict__.get('_param_array_', None) is None:
self._param_array_ = np.empty(self.size, dtype=np.float64)
return self._param_array_
@ -723,12 +782,58 @@ class Parameterizable(OptimizationHandlable):
def param_array(self, arr):
self._param_array_ = arr
def traverse(self, visit, *args, **kwargs):
"""
Traverse the hierarchy performing visit(self, *args, **kwargs)
at every node passed by downwards. This function includes self!
See "visitor pattern" in literature. This is implemented in pre-order fashion.
Example:
Collect all children:
children = []
self.traverse(children.append)
print children
"""
if not self.__visited:
visit(self, *args, **kwargs)
self.__visited = True
for c in self.parameters:
c.traverse(visit, *args, **kwargs)
self.__visited = False
def traverse_parents(self, visit, *args, **kwargs):
"""
Traverse the hierarchy upwards, visiting all parents and their children except self.
See "visitor pattern" in literature. This is implemented in pre-order fashion.
Example:
parents = []
self.traverse_parents(parents.append)
print parents
"""
if self.has_parent():
self.__visited = True
self._parent_._traverse_parents(visit, *args, **kwargs)
self.__visited = False
def _traverse_parents(self, visit, *args, **kwargs):
if not self.__visited:
self.__visited = True
visit(self, *args, **kwargs)
if self.has_parent():
self._parent_._traverse_parents(visit, *args, **kwargs)
self._parent_.traverse(visit, *args, **kwargs)
self.__visited = False
#=========================================================================
# Gradient handling
#=========================================================================
@property
def gradient(self):
if not hasattr(self, '_gradient_array_'):
if self.__dict__.get('_gradient_array_', None) is None:
self._gradient_array_ = np.empty(self.size, dtype=np.float64)
return self._gradient_array_
@ -738,7 +843,7 @@ class Parameterizable(OptimizationHandlable):
@property
def num_params(self):
return len(self._parameters_)
return len(self.parameters)
def _add_parameter_name(self, param, ignore_added_names=False):
pname = adjust_name_for_printing(param.name)
@ -771,130 +876,6 @@ class Parameterizable(OptimizationHandlable):
self._remove_parameter_name(None, old_name)
self._add_parameter_name(param)
def add_parameter(self, param, index=None, _ignore_added_names=False):
"""
:param parameters: the parameters to add
:type parameters: list of or one :py:class:`GPy.core.param.Param`
:param [index]: index of where to put parameters
:param bool _ignore_added_names: whether the name of the parameter overrides a possibly existing field
Add all parameters to this param class, you can insert parameters
at any given index using the :func:`list.insert` syntax
"""
if param in self._parameters_ and index is not None:
self.remove_parameter(param)
self.add_parameter(param, index)
# elif param.has_parent():
# raise HierarchyError, "parameter {} already in another model ({}), create new object (or copy) for adding".format(param._short(), param._highest_parent_._short())
elif param not in self._parameters_:
if param.has_parent():
parent = param._parent_
while parent is not None:
if parent is self:
raise HierarchyError, "You cannot add a parameter twice into the hierarchy"
parent = parent._parent_
param._parent_.remove_parameter(param)
# make sure the size is set
if index is None:
self.constraints.update(param.constraints, self.size)
self.priors.update(param.priors, self.size)
self._parameters_.append(param)
else:
start = sum(p.size for p in self._parameters_[:index])
self.constraints.shift_right(start, param.size)
self.priors.shift_right(start, param.size)
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)
parent = self
while parent is not None:
parent.size += param.size
parent = parent._parent_
self._connect_parameters()
self._highest_parent_._connect_parameters(ignore_added_names=_ignore_added_names)
self._highest_parent_._notify_parent_change()
self._highest_parent_._connect_fixes()
else:
raise HierarchyError, """Parameter exists already and no copy made"""
def add_parameters(self, *parameters):
"""
convenience method for adding several
parameters without gradient specification
"""
[self.add_parameter(p) for p in parameters]
def remove_parameter(self, param):
"""
:param param: param object to remove from being a parameter of this parameterized object.
"""
if not param in self._parameters_:
raise RuntimeError, "Parameter {} does not belong to this object, remove parameters directly from their respective parents".format(param._short())
start = sum([p.size for p in self._parameters_[:param._parent_index_]])
self._remove_parameter_name(param)
self.size -= param.size
del self._parameters_[param._parent_index_]
param._disconnect_parent()
param.remove_observer(self, self._pass_through_notify_observers)
self.constraints.shift_left(start, param.size)
self._connect_parameters()
self._notify_parent_change()
parent = self._parent_
while parent is not None:
parent.size -= param.size
parent = parent._parent_
self._highest_parent_._connect_parameters()
self._highest_parent_._connect_fixes()
self._highest_parent_._notify_parent_change()
def _connect_parameters(self, ignore_added_names=False):
# connect parameterlist to this parameterized object
# This just sets up the right connection for the params objects
# 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
old_size = 0
self.param_array = np.empty(self.size, dtype=np.float64)
self._gradient_array_ = np.empty(self.size, dtype=np.float64)
self._param_slices_ = []
for i, p in enumerate(self._parameters_):
p._parent_ = self
p._parent_index_ = i
pslice = slice(old_size, old_size + p.size)
# first connect all children
p._propagate_param_grad(self.param_array[pslice], self.full_gradient[pslice])
# then connect children to self
self.param_array[pslice] = p.param_array.flat # , requirements=['C', 'W']).ravel(order='C')
self.full_gradient[pslice] = p.full_gradient.flat # , requirements=['C', 'W']).ravel(order='C')
if not p.param_array.flags['C_CONTIGUOUS']:
raise ValueError, "This should not happen! Please write an email to the developers with the code, which reproduces this error. All parameter arrays must be C_CONTIGUOUS"
p.param_array.data = self.param_array[pslice].data
p.full_gradient.data = self.full_gradient[pslice].data
self._param_slices_.append(pslice)
self._add_parameter_name(p, ignore_added_names=ignore_added_names)
old_size += p.size
#===========================================================================
# notification system
#===========================================================================
@ -904,30 +885,13 @@ class Parameterizable(OptimizationHandlable):
self.notify_observers(which=which)
#===========================================================================
# Pickling
#===========================================================================
def __setstate__(self, state):
super(Parameterizable, self).__setstate__(state)
self._connect_parameters()
self._connect_fixes()
self._notify_parent_change()
self.parameters_changed()
def copy(self):
c = super(Parameterizable, self).copy()
c._connect_parameters()
c._connect_fixes()
c._notify_parent_change()
return c
#===========================================================================
# From being parentable, we have to define the parent_change notification
#===========================================================================
def _notify_parent_change(self):
"""
Notify all parameters that the parent has changed
"""
for p in self._parameters_:
for p in self.parameters:
p._parent_changed(self)
def parameters_changed(self):

223
GPy/core/parameterization/parameterized.py
View file

@ -3,13 +3,10 @@
import numpy; np = numpy
import cPickle
import itertools
from re import compile, _pattern_type
from param import ParamConcatenation
from parameter_core import Pickleable, Parameterizable, adjust_name_for_printing
from transformations import __fixed__
from lists_and_dicts import ArrayList
from parameter_core import HierarchyError, Parameterizable, adjust_name_for_printing
class ParametersChangedMeta(type):
def __call__(self, *args, **kw):
@ -68,8 +65,7 @@ class Parameterized(Parameterizable):
def __init__(self, name=None, parameters=[], *a, **kw):
super(Parameterized, self).__init__(name=name, *a, **kw)
self._in_init_ = True
self._parameters_ = ArrayList()
self.size = sum(p.size for p in self._parameters_)
self.size = sum(p.size for p in self.parameters)
self.add_observer(self, self._parameters_changed_notification, -100)
if not self._has_fixes():
self._fixes_ = None
@ -82,15 +78,15 @@ class Parameterized(Parameterizable):
import pydot # @UnresolvedImport
iamroot = False
if G is None:
G = pydot.Dot(graph_type='digraph')
G = pydot.Dot(graph_type='digraph', bgcolor=None)
iamroot=True
node = pydot.Node(id(self), shape='record', label=self.name)
node = pydot.Node(id(self), shape='box', label=self.name)#, color='white')
G.add_node(node)
for child in self._parameters_:
for child in self.parameters:
child_node = child.build_pydot(G)
G.add_edge(pydot.Edge(node, child_node))
G.add_edge(pydot.Edge(node, child_node))#, color='white'))
for o in self.observers.keys():
for _, o, _ in self.observers:
label = o.name if hasattr(o, 'name') else str(o)
observed_node = pydot.Node(id(o), label=label)
G.add_node(observed_node)
@ -102,58 +98,133 @@ class Parameterized(Parameterizable):
return node
#===========================================================================
# Gradient control
# Add remove parameters:
#===========================================================================
def _transform_gradients(self, g):
if self.has_parent():
return g
[numpy.put(g, i, g[i] * c.gradfactor(self.param_array[i])) for c, i in self.constraints.iteritems() if c != __fixed__]
if self._has_fixes(): return g[self._fixes_]
return g
#===========================================================================
# Indexable
#===========================================================================
def _offset_for(self, param):
# get the offset in the parameterized index array for param
if param.has_parent():
if param._parent_._get_original(param) in self._parameters_:
return self._param_slices_[param._parent_._get_original(param)._parent_index_].start
return self._offset_for(param._parent_) + param._parent_._offset_for(param)
return 0
def _raveled_index_for(self, param):
def add_parameter(self, param, index=None, _ignore_added_names=False):
"""
get the raveled index for a param
that is an int array, containing the indexes for the flattened
param inside this parameterized logic.
"""
if isinstance(param, ParamConcatenation):
return numpy.hstack((self._raveled_index_for(p) for p in param.params))
return param._raveled_index() + self._offset_for(param)
:param parameters: the parameters to add
:type parameters: list of or one :py:class:`GPy.core.param.Param`
:param [index]: index of where to put parameters
def _raveled_index(self):
"""
get the raveled index for this object,
this is not in the global view of things!
"""
return numpy.r_[:self.size]
:param bool _ignore_added_names: whether the name of the parameter overrides a possibly existing field
#===========================================================================
# Convenience for fixed, tied checking of param:
#===========================================================================
@property
def is_fixed(self):
for p in self._parameters_:
if not p.is_fixed: return False
return True
Add all parameters to this param class, you can insert parameters
at any given index using the :func:`list.insert` syntax
"""
if param in self.parameters and index is not None:
self.remove_parameter(param)
self.add_parameter(param, index)
# elif param.has_parent():
# raise HierarchyError, "parameter {} already in another model ({}), create new object (or copy) for adding".format(param._short(), param._highest_parent_._short())
elif param not in self.parameters:
if param.has_parent():
def visit(parent, self):
if parent is self:
raise HierarchyError, "You cannot add a parameter twice into the hierarchy"
param.traverse_parents(visit, self)
param._parent_.remove_parameter(param)
# make sure the size is set
if index is None:
self.constraints.update(param.constraints, self.size)
self.priors.update(param.priors, self.size)
self.parameters.append(param)
else:
start = sum(p.size for p in self.parameters[:index])
self.constraints.shift_right(start, param.size)
self.priors.shift_right(start, param.size)
self.constraints.update(param.constraints, start)
self.priors.update(param.priors, start)
self.parameters.insert(index, param)
def _get_original(self, param):
# if advanced indexing is activated it happens that the array is a copy
# you can retrieve the original param through this method, by passing
# the copy here
return self._parameters_[param._parent_index_]
param.add_observer(self, self._pass_through_notify_observers, -np.inf)
parent = self
while parent is not None:
parent.size += param.size
parent = parent._parent_
self._connect_parameters()
self._highest_parent_._connect_parameters(ignore_added_names=_ignore_added_names)
self._highest_parent_._notify_parent_change()
self._highest_parent_._connect_fixes()
else:
raise HierarchyError, """Parameter exists already and no copy made"""
def add_parameters(self, *parameters):
"""
convenience method for adding several
parameters without gradient specification
"""
[self.add_parameter(p) for p in parameters]
def remove_parameter(self, param):
"""
:param param: param object to remove from being a parameter of this parameterized object.
"""
if not param in self.parameters:
raise RuntimeError, "Parameter {} does not belong to this object {}, remove parameters directly from their respective parents".format(param._short(), self.name)
start = sum([p.size for p in self.parameters[:param._parent_index_]])
self._remove_parameter_name(param)
self.size -= param.size
del self.parameters[param._parent_index_]
param._disconnect_parent()
param.remove_observer(self, self._pass_through_notify_observers)
self.constraints.shift_left(start, param.size)
self._connect_parameters()
self._notify_parent_change()
parent = self._parent_
while parent is not None:
parent.size -= param.size
parent = parent._parent_
self._highest_parent_._connect_parameters()
self._highest_parent_._connect_fixes()
self._highest_parent_._notify_parent_change()
def _connect_parameters(self, ignore_added_names=False):
# connect parameterlist to this parameterized object
# This just sets up the right connection for the params objects
# 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
if self.param_array.size != self.size:
self.param_array = np.empty(self.size, dtype=np.float64)
if self.gradient.size != self.size:
self._gradient_array_ = np.empty(self.size, dtype=np.float64)
old_size = 0
self._param_slices_ = []
for i, p in enumerate(self.parameters):
p._parent_ = self
p._parent_index_ = i
pslice = slice(old_size, old_size + p.size)
# first connect all children
p._propagate_param_grad(self.param_array[pslice], self.gradient_full[pslice])
# then connect children to self
self.param_array[pslice] = p.param_array.flat # , requirements=['C', 'W']).ravel(order='C')
self.gradient_full[pslice] = p.gradient_full.flat # , requirements=['C', 'W']).ravel(order='C')
if not p.param_array.flags['C_CONTIGUOUS']:
raise ValueError, "This should not happen! Please write an email to the developers with the code, which reproduces this error. All parameter arrays must be C_CONTIGUOUS"
p.param_array.data = self.param_array[pslice].data
p.gradient_full.data = self.gradient_full[pslice].data
self._param_slices_.append(pslice)
self._add_parameter_name(p, ignore_added_names=ignore_added_names)
old_size += p.size
#===========================================================================
# Get/set parameters:
@ -200,10 +271,28 @@ class Parameterized(Parameterizable):
def __setattr__(self, name, val):
# override the default behaviour, if setting a param, so broadcasting can by used
if hasattr(self, "_parameters_"):
if hasattr(self, "parameters"):
pnames = self.parameter_names(False, adjust_for_printing=True, recursive=False)
if name in pnames: self._parameters_[pnames.index(name)][:] = val; return
if name in pnames: self.parameters[pnames.index(name)][:] = val; return
object.__setattr__(self, name, val);
#===========================================================================
# Pickling
#===========================================================================
def __setstate__(self, state):
super(Parameterized, self).__setstate__(state)
self._connect_parameters()
self._connect_fixes()
self._notify_parent_change()
self.parameters_changed()
def copy(self):
c = super(Parameterized, self).copy()
c._connect_parameters()
c._connect_fixes()
c._notify_parent_change()
return c
#===========================================================================
# Printing:
#===========================================================================
@ -211,22 +300,22 @@ class Parameterized(Parameterizable):
return self.hierarchy_name()
@property
def flattened_parameters(self):
return [xi for x in self._parameters_ for xi in x.flattened_parameters]
return [xi for x in self.parameters for xi in x.flattened_parameters]
@property
def _parameter_sizes_(self):
return [x.size for x in self._parameters_]
return [x.size for x in self.parameters]
@property
def parameter_shapes(self):
return [xi for x in self._parameters_ for xi in x.parameter_shapes]
return [xi for x in self.parameters for xi in x.parameter_shapes]
@property
def _constraints_str(self):
return [cs for p in self._parameters_ for cs in p._constraints_str]
return [cs for p in self.parameters for cs in p._constraints_str]
@property
def _priors_str(self):
return [cs for p in self._parameters_ for cs in p._priors_str]
return [cs for p in self.parameters for cs in p._priors_str]
@property
def _description_str(self):
return [xi for x in self._parameters_ for xi in x._description_str]
return [xi for x in self.parameters for xi in x._description_str]
@property
def _ties_str(self):
return [','.join(x._ties_str) for x in self.flattened_parameters]
@ -246,7 +335,7 @@ class Parameterized(Parameterizable):
to_print = []
for n, d, c, t, p in itertools.izip(names, desc, constrs, ts, prirs):
to_print.append(format_spec.format(name=n, desc=d, const=c, t=t, pri=p))
# 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)]
# 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 + + pl + tl + 8 * 2 + 3)
if header:
header = " {{0:<{0}s}} | {{1:^{1}s}} | {{2:^{2}s}} | {{3:^{3}s}} | {{4:^{4}s}}".format(nl, sl, cl, pl, tl).format(name, "Value", "Constraint", "Prior", "Tied to")

19
GPy/core/parameterization/variational.py
View file

@ -81,7 +81,7 @@ class VariationalPosterior(Parameterized):
def _raveled_index(self):
index = np.empty(dtype=int, shape=0)
size = 0
for p in self._parameters_:
for p in self.parameters:
index = np.hstack((index, p._raveled_index()+size))
size += p._realsize_ if hasattr(p, '_realsize_') else p.size
return index
@ -96,10 +96,13 @@ class VariationalPosterior(Parameterized):
dc = self.__dict__.copy()
dc['mean'] = self.mean[s]
dc['variance'] = self.variance[s]
dc['_parameters_'] = copy.copy(self._parameters_)
dc['parameters'] = copy.copy(self.parameters)
n.__dict__.update(dc)
n._parameters_[dc['mean']._parent_index_] = dc['mean']
n._parameters_[dc['variance']._parent_index_] = dc['variance']
n.parameters[dc['mean']._parent_index_] = dc['mean']
n.parameters[dc['variance']._parent_index_] = dc['variance']
n._gradient_array_ = None
oversize = self.size - self.mean.size - self.variance.size
n.size = n.mean.size + n.variance.size + oversize
n.ndim = n.mean.ndim
n.shape = n.mean.shape
n.num_data = n.mean.shape[0]
@ -147,11 +150,11 @@ class SpikeAndSlabPosterior(VariationalPosterior):
dc['mean'] = self.mean[s]
dc['variance'] = self.variance[s]
dc['binary_prob'] = self.binary_prob[s]
dc['_parameters_'] = copy.copy(self._parameters_)
dc['parameters'] = copy.copy(self.parameters)
n.__dict__.update(dc)
n._parameters_[dc['mean']._parent_index_] = dc['mean']
n._parameters_[dc['variance']._parent_index_] = dc['variance']
n._parameters_[dc['binary_prob']._parent_index_] = dc['binary_prob']
n.parameters[dc['mean']._parent_index_] = dc['mean']
n.parameters[dc['variance']._parent_index_] = dc['variance']
n.parameters[dc['binary_prob']._parent_index_] = dc['binary_prob']
n.ndim = n.mean.ndim
n.shape = n.mean.shape
n.num_data = n.mean.shape[0]

17
GPy/core/sparse_gp.py
View file

@ -79,29 +79,32 @@ class SparseGP(GP):
self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
self.Z.gradient += self.kern.gradients_X(self.grad_dict['dL_dKnm'].T, self.Z, self.X)
def _raw_predict(self, Xnew, full_cov=False):
def _raw_predict(self, Xnew, full_cov=False, kern=None):
"""
Make a prediction for the latent function values
"""
if kern is None: kern = self.kern
if not isinstance(Xnew, VariationalPosterior):
Kx = self.kern.K(self.Z, Xnew)
Kx = kern.K(self.Z, Xnew)
mu = np.dot(Kx.T, self.posterior.woodbury_vector)
if full_cov:
Kxx = self.kern.K(Xnew)
Kxx = kern.K(Xnew)
var = Kxx - np.dot(Kx.T, np.dot(self.posterior.woodbury_inv, Kx))
#var = Kxx[:,:,None] - np.tensordot(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx).T, Kx, [1,0]).swapaxes(1,2)
var = var.squeeze()
else:
Kxx = self.kern.Kdiag(Xnew)
Kxx = kern.Kdiag(Xnew)
var = (Kxx - np.sum(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx) * Kx[None,:,:], 1)).T
else:
Kx = self.kern.psi1(self.Z, Xnew)
Kx = kern.psi1(self.Z, Xnew)
mu = np.dot(Kx, self.posterior.woodbury_vector)
if full_cov:
raise NotImplementedError, "TODO"
else:
Kxx = self.kern.psi0(self.Z, Xnew)
psi2 = self.kern.psi2(self.Z, Xnew)
Kxx = kern.psi0(self.Z, Xnew)
psi2 = kern.psi2(self.Z, Xnew)
var = Kxx - np.sum(np.sum(psi2 * Kmmi_LmiBLmi[None, :, :], 1), 1)
return mu, var