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Merged parameterized fixing

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Alan Saul 2014-02-10 15:42:47 +00:00
commit b72d319909
41 changed files with 248 additions and 270 deletions
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13
GPy/core/model.py
View file

@ -439,18 +439,19 @@ class Model(Parameterized):
print "No free parameters to check"
return
gradient = self.objective_function_gradients(x)
np.where(gradient==0, 1e-312, gradient)
for i in param_list:
xx = x.copy()
xx[i] += step
f1, g1 = self.objective_and_gradients(xx)
xx[i] -= 2.*step
f2, g2 = self.objective_and_gradients(xx)
gradient = self.objective_function_gradients(x)[i]
numerical_gradient = (f1 - f2) / (2 * step)
ratio = (f1 - f2) / (2 * step * np.where(gradient==0, 1e-312, gradient))
difference = np.abs((f1 - f2) / 2 / step - gradient)
ratio = (f1 - f2) / (2 * step * gradient[i])
difference = np.abs((f1 - f2) / 2 / step - gradient[i])
if (np.abs(1. - ratio) < tolerance) or np.abs(difference) < tolerance:
formatted_name = "\033[92m {0} \033[0m".format(names[i])
@ -458,7 +459,7 @@ class Model(Parameterized):
formatted_name = "\033[91m {0} \033[0m".format(names[i])
r = '%.6f' % float(ratio)
d = '%.6f' % float(difference)
g = '%.6f' % gradient
g = '%.6f' % gradient[i]
ng = '%.6f' % float(numerical_gradient)
grad_string = "{0:<{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}".format(formatted_name, r, d, g, ng, c0=cols[0] + 9, c1=cols[1], c2=cols[2], c3=cols[3], c4=cols[4])
print grad_string

37
GPy/core/parameterization/array_core.py
View file

@ -15,8 +15,18 @@ class ListArray(np.ndarray):
def __new__(cls, input_array):
obj = np.asanyarray(input_array).view(cls)
return obj
def __eq__(self, other):
return other is self
#def __eq__(self, other):
# return other is self
class ParamList(list):
def __contains__(self, other):
for el in self:
if el is other:
return True
return False
pass
class ObservableArray(ListArray, Observable):
"""
@ -36,16 +46,19 @@ class ObservableArray(ListArray, Observable):
if obj is None: return
self._observers_ = getattr(obj, '_observers_', None)
def __setitem__(self, s, val, update=True):
if self.ndim:
if not np.all(np.equal(self[s], val)):
super(ObservableArray, self).__setitem__(s, val)
if update:
self._notify_observers()
else:
if not np.all(np.equal(self, val)):
super(ObservableArray, self).__setitem__(Ellipsis, val)
if update:
self._notify_observers()
super(ObservableArray, self).__setitem__(s, val)
if update:
self._notify_observers()
# if self.ndim:
# if not np.all(np.equal(self[s], val)):
# super(ObservableArray, self).__setitem__(s, val)
# if update:
# self._notify_observers()
# else:
# if not np.all(np.equal(self, val)):
# super(ObservableArray, self).__setitem__(Ellipsis, val)
# if update:
# self._notify_observers()
def __getslice__(self, start, stop):
return self.__getitem__(slice(start, stop))
def __setslice__(self, start, stop, val):

64
GPy/core/parameterization/index_operations.py
View file

@ -90,11 +90,6 @@ class ParameterIndexOperations(object):
return self._properties.values()
def properties_for(self, index):
# already_seen = dict()
# for ni in index:
# if ni not in already_seen:
# already_seen[ni] = [prop for prop in self.iter_properties() if ni in self._properties[prop]]
# yield already_seen[ni]
return vectorize(lambda i: [prop for prop in self.iter_properties() if i in self._properties[prop]], otypes=[list])(index)
def add(self, prop, indices):
@ -111,70 +106,11 @@ class ParameterIndexOperations(object):
self._properties[prop] = diff
else:
del self._properties[prop]
#[self._reverse[i].remove(prop) for i in removed if prop in self._reverse[i]]
return removed.astype(int)
# else:
# for a in self.properties():
# if numpy.all(a==prop) and a._parent_index_ == prop._parent_index_:
# ind = create_raveled_indices(indices, shape, offset)
# diff = remove_indices(self[a], ind)
# removed = numpy.intersect1d(self[a], ind, True)
# if not index_empty(diff):
# self._properties[a] = diff
# else:
# del self._properties[a]
# [self._reverse[i].remove(a) for i in removed if a in self._reverse[i]]
# return removed.astype(int)
return numpy.array([]).astype(int)
def __getitem__(self, prop):
return self._properties[prop]
# class TieIndexOperations(object):
# def __init__(self, params):
# self.params = params
# self.tied_from = ParameterIndexOperations()
# self.tied_to = ParameterIndexOperations()
# def add(self, tied_from, tied_to):
# rav_from = self.params._raveled_index_for(tied_from)
# rav_to = self.params._raveled_index_for(tied_to)
# self.tied_from.add(tied_to, rav_from)
# self.tied_to.add(tied_to, rav_to)
# return rav_from, rav_to
# def remove(self, tied_from, tied_to):
# rav_from = self.params._raveled_index_for(tied_from)
# rav_to = self.params._raveled_index_for(tied_to)
# rem_from = self.tied_from.remove(tied_to, rav_from)
# rem_to = self.tied_to.remove(tied_to, rav_to)
# left_from = self.tied_from._properties.pop(tied_to)
# left_to = self.tied_to._properties.pop(tied_to)
# self.tied_from[numpy.delete(tied_to, rem_from)] = left_from
# self.tied_to[numpy.delete(tied_to, rem_to)] = left_to
# return rav_from, rav_to
# def from_to_for(self, index):
# return self.tied_from.properties_for(index), self.tied_to.properties_for(index)
# def iter_from_to_indices(self):
# for k, f in self.tied_from.iteritems():
# yield f, self.tied_to[k]
# def iter_to_indices(self):
# return self.tied_to.iterindices()
# def iter_from_indices(self):
# return self.tied_from.iterindices()
# def iter_from_items(self):
# for f, i in self.tied_from.iteritems():
# yield f, i
# def iter_properties(self):
# return self.tied_from.iter_properties()
# def properties(self):
# return self.tied_from.properties()
# def from_to_indices(self, param):
# return self.tied_from[param], self.tied_to[param]
#
# # def create_raveled_indices(index, shape, offset=0):
# # if isinstance(index, (tuple, list)): i = [slice(None)] + list(index)
# # else: i = [slice(None), index]
# # ind = numpy.array(numpy.ravel_multi_index(numpy.indices(shape)[i], shape)).flat + numpy.int_(offset)
# # return ind
def combine_indices(arr1, arr2):
return numpy.union1d(arr1, arr2)

85
GPy/core/parameterization/param.py
View file

@ -75,7 +75,6 @@ class Param(ObservableArray, Constrainable):
super(Param, self).__array_finalize__(obj)
self._direct_parent_ = getattr(obj, '_direct_parent_', None)
self._parent_index_ = getattr(obj, '_parent_index_', None)
self._highest_parent_ = getattr(obj, '_highest_parent_', None)
self._current_slice_ = getattr(obj, '_current_slice_', None)
self._tied_to_me_ = getattr(obj, '_tied_to_me_', None)
self._tied_to_ = getattr(obj, '_tied_to_', None)
@ -98,7 +97,6 @@ class Param(ObservableArray, Constrainable):
(self.name,
self._direct_parent_,
self._parent_index_,
self._highest_parent_,
self._current_slice_,
self._realshape_,
self._realsize_,
@ -119,7 +117,6 @@ class Param(ObservableArray, Constrainable):
self._realsize_ = state.pop()
self._realshape_ = state.pop()
self._current_slice_ = state.pop()
self._highest_parent_ = state.pop()
self._parent_index_ = state.pop()
self._direct_parent_ = state.pop()
self.name = state.pop()
@ -153,8 +150,6 @@ class Param(ObservableArray, Constrainable):
@property
def _parameters_(self):
return []
def _connect_highest_parent(self, highest_parent):
self._highest_parent_ = highest_parent
def _collect_gradient(self, target):
target[:] = self.gradient.flat
#===========================================================================
@ -178,7 +173,7 @@ class Param(ObservableArray, Constrainable):
# this tie will be created to the parameter the param is tied
# to.
assert isinstance(param, Param), "Argument {1} not of type {0}".format(Param,param.__class__)
assert isinstance(param, Param), "Argument {1} not of type {0}".format(Param, param.__class__)
param = numpy.atleast_1d(param)
if param.size != 1:
raise NotImplementedError, "Broadcast tying is not implemented yet"
@ -231,7 +226,7 @@ class Param(ObservableArray, Constrainable):
t_rav_i = t._raveled_index()
tr_rav_i = tied_to_me._raveled_index()
new_index = list(set(t_rav_i) | set(tr_rav_i))
tmp = t._direct_parent_._get_original(t)[numpy.unravel_index(new_index,t._realshape_)]
tmp = t._direct_parent_._get_original(t)[numpy.unravel_index(new_index, t._realshape_)]
self._tied_to_me_[tmp] = self._tied_to_me_[t] | set(self._raveled_index())
del self._tied_to_me_[t]
return
@ -244,7 +239,7 @@ class Param(ObservableArray, Constrainable):
import ipdb;ipdb.set_trace()
new_index = list(set(t_rav_i) - set(tr_rav_i))
if new_index:
tmp = t._direct_parent_._get_original(t)[numpy.unravel_index(new_index,t._realshape_)]
tmp = t._direct_parent_._get_original(t)[numpy.unravel_index(new_index, t._realshape_)]
self._tied_to_me_[tmp] = self._tied_to_me_[t]
del self._tied_to_me_[t]
if len(self._tied_to_me_[tmp]) == 0:
@ -252,12 +247,12 @@ class Param(ObservableArray, Constrainable):
else:
del self._tied_to_me_[t]
def _on_tied_parameter_changed(self, val, ind):
if not self._updated_: #not fast_array_equal(self, val[ind]):
if not self._updated_: # not fast_array_equal(self, val[ind]):
val = numpy.atleast_1d(val)
self._updated_ = True
if self._original_:
self.__setitem__(slice(None), val[ind], update=False)
else: # this happens when indexing created a copy of the array
else: # this happens when indexing created a copy of the array
self._direct_parent_._get_original(self).__setitem__(self._current_slice_, val[ind], update=False)
self._notify_tied_parameters()
self._updated_ = False
@ -286,11 +281,11 @@ class Param(ObservableArray, Constrainable):
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:
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
except AttributeError: pass# returning 0d array or float, double etc
except AttributeError: pass # returning 0d array or float, double etc
return new_arr
def __setitem__(self, s, val, update=True):
super(Param, self).__setitem__(s, val, update=update)
@ -311,8 +306,8 @@ class Param(ObservableArray, Constrainable):
elif isinstance(si, (list,numpy.ndarray,tuple)):
a = si[0]
else: a = si
if a<0:
a = self._realshape_[i]+a
if a < 0:
a = self._realshape_[i] + a
internal_offset += a * extended_realshape[i]
return internal_offset
def _raveled_index(self, slice_index=None):
@ -320,8 +315,8 @@ class Param(ObservableArray, Constrainable):
# of this object
extended_realshape = numpy.cumprod((1,) + self._realshape_[:0:-1])[::-1]
ind = self._indices(slice_index)
if ind.ndim < 2: ind=ind[:,None]
return numpy.asarray(numpy.apply_along_axis(lambda x: numpy.sum(extended_realshape*x), 1, ind), dtype=int)
if ind.ndim < 2: ind = ind[:, None]
return numpy.asarray(numpy.apply_along_axis(lambda x: numpy.sum(extended_realshape * x), 1, ind), dtype=int)
def _expand_index(self, slice_index=None):
# this calculates the full indexing arrays from the slicing objects given by get_item for _real..._ attributes
# it basically translates slices to their respective index arrays and turns negative indices around
@ -334,11 +329,11 @@ class Param(ObservableArray, Constrainable):
if isinstance(a, slice):
start, stop, step = a.indices(b)
return numpy.r_[start:stop:step]
elif isinstance(a, (list,numpy.ndarray,tuple)):
elif isinstance(a, (list, numpy.ndarray, tuple)):
a = numpy.asarray(a, dtype=int)
a[a<0] = b + a[a<0]
elif a<0:
a = b+a
a[a < 0] = b + a[a < 0]
elif a < 0:
a = b + a
return numpy.r_[a]
return numpy.r_[:b]
return itertools.imap(f, itertools.izip_longest(slice_index[:self._realndim_], self._realshape_, fillvalue=slice(self.size)))
@ -362,7 +357,7 @@ class Param(ObservableArray, Constrainable):
#===========================================================================
@property
def _description_str(self):
if self.size <= 1: return ["%f"%self]
if self.size <= 1: return ["%f" % self]
else: return [str(self.shape)]
def _parameter_names(self, add_name):
return [self.name]
@ -374,31 +369,31 @@ class Param(ObservableArray, Constrainable):
return [self.shape]
@property
def _constraints_str(self):
return [' '.join(map(lambda c: str(c[0]) if c[1].size==self._realsize_ else "{"+str(c[0])+"}", self._highest_parent_._constraints_iter_items(self)))]
return [' '.join(map(lambda c: str(c[0]) if c[1].size == self._realsize_ else "{" + str(c[0]) + "}", self._highest_parent_._constraints_iter_items(self)))]
@property
def _ties_str(self):
return [t._short() for t in self._tied_to_] or ['']
@property
def name_hirarchical(self):
if self.has_parent():
return self._direct_parent_.hirarchy_name()+adjust_name_for_printing(self.name)
return self._direct_parent_.hirarchy_name() + adjust_name_for_printing(self.name)
return adjust_name_for_printing(self.name)
def __repr__(self, *args, **kwargs):
name = "\033[1m{x:s}\033[0;0m:\n".format(
x=self.name_hirarchical)
return name + super(Param, self).__repr__(*args,**kwargs)
return name + super(Param, self).__repr__(*args, **kwargs)
def _ties_for(self, rav_index):
#size = sum(p.size for p in self._tied_to_)
# size = sum(p.size for p in self._tied_to_)
ties = numpy.empty(shape=(len(self._tied_to_), numpy.size(rav_index)), dtype=Param)
for i, tied_to in enumerate(self._tied_to_):
for t, ind in tied_to._tied_to_me_.iteritems():
if t._parent_index_ == self._parent_index_:
matches = numpy.where(rav_index[:,None] == t._raveled_index()[None, :])
matches = numpy.where(rav_index[:, None] == t._raveled_index()[None, :])
tt_rav_index = tied_to._raveled_index()
ind_rav_matches = numpy.where(tt_rav_index == numpy.array(list(ind)))[0]
if len(ind) != 1: ties[i, matches[0][ind_rav_matches]] = numpy.take(tt_rav_index, matches[1], mode='wrap')[ind_rav_matches]
else: ties[i, matches[0]] = numpy.take(tt_rav_index, matches[1], mode='wrap')
return map(lambda a: sum(a,[]), zip(*[[[tie.flatten()] if tx!=None else [] for tx in t] for t,tie in zip(ties,self._tied_to_)]))
return map(lambda a: sum(a, []), zip(*[[[tie.flatten()] if tx != None else [] for tx in t] for t, tie in zip(ties, self._tied_to_)]))
def _constraints_for(self, rav_index):
return self._highest_parent_._constraints_for(self, rav_index)
def _indices(self, slice_index=None):
@ -408,12 +403,12 @@ class Param(ObservableArray, Constrainable):
if isinstance(slice_index, (tuple, list)):
clean_curr_slice = [s for s in slice_index if numpy.any(s != Ellipsis)]
if (all(isinstance(n, (numpy.ndarray, list, tuple)) for n in clean_curr_slice)
and len(set(map(len,clean_curr_slice))) <= 1):
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_))
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),
dtype=[('',int)]*self._realndim_,count=reduce(lambda a,b: a*b.size,expanded_index,1)).view((int, self._realndim_))
dtype=[('', int)] * self._realndim_, count=reduce(lambda a, b: a * b.size, expanded_index, 1)).view((int, self._realndim_))
def _max_len_names(self, gen, header):
return reduce(lambda a, b:max(a, len(b)), gen, len(header))
def _max_len_values(self):
@ -426,9 +421,9 @@ class Param(ObservableArray, Constrainable):
if self._realsize_ < 2:
return name
ind = self._indices()
if ind.size > 4: indstr = ','.join(map(str,ind[:2])) + "..." + ','.join(map(str,ind[-2:]))
else: indstr = ','.join(map(str,ind))
return name+'['+indstr+']'
if ind.size > 4: indstr = ','.join(map(str, ind[:2])) + "..." + ','.join(map(str, ind[-2:]))
else: indstr = ','.join(map(str, ind))
return name + '[' + indstr + ']'
def __str__(self, constr_matrix=None, indices=None, ties=None, lc=None, lx=None, li=None, lt=None):
filter_ = self._current_slice_
vals = self.flat
@ -441,10 +436,10 @@ class Param(ObservableArray, Constrainable):
if lx is None: lx = self._max_len_values()
if li is None: li = self._max_len_index(indices)
if lt is None: lt = self._max_len_names(ties, __tie_name__)
header = " {i:^{2}s} | \033[1m{x:^{1}s}\033[0;0m | {c:^{0}s} | {t:^{3}s}".format(lc,lx,li,lt, x=self.name_hirarchical, c=__constraints_name__, i=__index_name__, t=__tie_name__) # nice header for printing
header = " {i:^{2}s} | \033[1m{x:^{1}s}\033[0;0m | {c:^{0}s} | {t:^{3}s}".format(lc, lx, li, lt, x=self.name_hirarchical, c=__constraints_name__, i=__index_name__, t=__tie_name__) # nice header for printing
if not ties: ties = itertools.cycle([''])
return "\n".join([header]+[" {i!s:^{3}s} | {x: >{1}.{2}g} | {c:^{0}s} | {t:^{4}s} ".format(lc,lx,__precision__,li,lt, x=x, c=" ".join(map(str,c)), t=(t or ''), i=i) for i,x,c,t in itertools.izip(indices,vals,constr_matrix,ties)]) # return all the constraints with right indices
#except: return super(Param, self).__str__()
return "\n".join([header] + [" {i!s:^{3}s} | {x: >{1}.{2}g} | {c:^{0}s} | {t:^{4}s} ".format(lc, lx, __precision__, li, lt, x=x, c=" ".join(map(str, c)), t=(t or ''), i=i) for i, x, c, t in itertools.izip(indices, vals, constr_matrix, ties)]) # return all the constraints with right indices
# except: return super(Param, self).__str__()
class ParamConcatenation(object):
def __init__(self, params):
@ -455,8 +450,8 @@ class ParamConcatenation(object):
See :py:class:`GPy.core.parameter.Param` for more details on constraining.
"""
#self.params = params
self.params = []
# self.params = params
self.params = ParamList([])
for p in params:
for p in p.flattened_parameters:
if p not in self.params:
@ -535,12 +530,12 @@ class ParamConcatenation(object):
def untie(self, *ties):
[param.untie(*ties) for param in self.params]
__lt__ = lambda self, val: self._vals()<val
__le__ = lambda self, val: self._vals()<=val
__eq__ = lambda self, val: self._vals()==val
__ne__ = lambda self, val: self._vals()!=val
__gt__ = lambda self, val: self._vals()>val
__ge__ = lambda self, val: self._vals()>=val
__lt__ = lambda self, val: self._vals() < val
__le__ = lambda self, val: self._vals() <= val
__eq__ = lambda self, val: self._vals() == val
__ne__ = lambda self, val: self._vals() != val
__gt__ = lambda self, val: self._vals() > val
__ge__ = lambda self, val: self._vals() >= val
def __str__(self, *args, **kwargs):
def f(p):
ind = p._raveled_index()
@ -569,7 +564,7 @@ if __name__ == '__main__':
print "random done"
p = Param("q_mean", X)
p1 = Param("q_variance", numpy.random.rand(*p.shape))
p2 = Param("Y", numpy.random.randn(p.shape[0],1))
p2 = Param("Y", numpy.random.randn(p.shape[0], 1))
p3 = Param("variance", numpy.random.rand())
p4 = Param("lengthscale", numpy.random.rand(2))

18
GPy/core/parameterization/parameter_core.py
View file

@ -48,19 +48,25 @@ class Pickleable(object):
#===============================================================================
class Parentable(object):
def __init__(self, direct_parent=None, highest_parent=None, parent_index=None):
super(Parentable,self).__init__()
def __init__(self, direct_parent=None, parent_index=None):
super(Parentable,self).__init__()
self._direct_parent_ = direct_parent
self._parent_index_ = parent_index
self._highest_parent_ = highest_parent
def has_parent(self):
return self._direct_parent_ is not None and self._highest_parent_ is not None
return self._direct_parent_ is not None
@property
def _highest_parent_(self):
if self._direct_parent_ is None:
return self
return self._direct_parent_._highest_parent_
class Nameable(Parentable):
_name = None
def __init__(self, name, direct_parent=None, highest_parent=None, parent_index=None):
super(Nameable,self).__init__(direct_parent, highest_parent, parent_index)
def __init__(self, name, direct_parent=None, parent_index=None):
super(Nameable,self).__init__(direct_parent, parent_index)
self._name = name or self.__class__.__name__
@property

27
GPy/core/parameterization/parameterized.py
View file

@ -11,6 +11,7 @@ from param import ParamConcatenation, Param
from parameter_core import Constrainable, Pickleable, Observable, adjust_name_for_printing
from index_operations import ParameterIndexOperations,\
index_empty
from array_core import ParamList
#===============================================================================
# Printing:
@ -69,7 +70,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
super(Parameterized, self).__init__(name=name)
self._in_init_ = True
self._constraints_ = None#ParameterIndexOperations()
self._parameters_ = []
self._parameters_ = ParamList()
self.size = sum(p.size for p in self._parameters_)
if not self._has_fixes():
self._fixes_ = None
@ -188,10 +189,10 @@ class Parameterized(Constrainable, Pickleable, Observable):
note: if it is a string object it will not (!) be regexp-matched
automatically.
"""
self._parameters_ = [p for p in self._parameters_
self._parameters_ = ParamList([p for p in self._parameters_
if not (p._parent_index_ in names_params_indices
or p.name in names_params_indices
or p in names_params_indices)]
or p in names_params_indices)])
self._connect_parameters()
def parameters_changed(self):
@ -216,7 +217,6 @@ class Parameterized(Constrainable, Pickleable, Observable):
for i,p in enumerate(self._parameters_):
p._direct_parent_ = self
p._parent_index_ = i
p._connect_highest_parent(self)
not_unique = []
sizes.append(p.size+sizes[-1])
self._param_slices_.append(slice(sizes[-2], sizes[-1]))
@ -230,15 +230,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
elif not (pname in not_unique):
self.__dict__[pname] = p
self._added_names_.add(pname)
def _connect_highest_parent(self, highest_parent):
self._highest_parent_ = highest_parent
if not hasattr(self, "_parameters_") or len(self._parameters_) < 1:
# no parameters for this class
return
for p in self._parameters_:
p._connect_highest_parent(highest_parent)
#===========================================================================
# Pickling operations
#===========================================================================
@ -382,6 +374,8 @@ class Parameterized(Constrainable, Pickleable, Observable):
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)
def _raveled_index(self):
@ -419,10 +413,10 @@ class Parameterized(Constrainable, Pickleable, Observable):
#===========================================================================
# Fixing parameters:
#===========================================================================
def _fix(self, param, warning=True, update=True):
def _fix(self, param, warning=True):
f = self._add_constrain(param, __fixed__, warning)
self._set_fixed(f)
def _unfix(self, param, update=True):
def _unfix(self, param):
if self._has_fixes():
f = self._remove_constrain(param, __fixed__)
self._set_unfixed(f)
@ -446,8 +440,7 @@ class Parameterized(Constrainable, Pickleable, Observable):
# 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_]
return param._direct_parent_._parameters_[param._parent_index_]
return self._parameters_[param._parent_index_]
def hirarchy_name(self):
if self.has_parent():
return self._direct_parent_.hirarchy_name() + adjust_name_for_printing(self.name) + "."

5
GPy/core/parameterization/variational.py
View file

@ -3,10 +3,8 @@ Created on 6 Nov 2013
@author: maxz
'''
import numpy as np
from parameterized import Parameterized
from param import Param
from ...util.misc import param_to_array
class Normal(Parameterized):
'''
@ -26,6 +24,7 @@ class Normal(Parameterized):
See GPy.plotting.matplot_dep.variational_plots
"""
import sys
assert "matplotlib" in sys.modules, "matplotlib package has not been imported."
from ..plotting.matplot_dep import variational_plots
from ...plotting.matplot_dep import variational_plots
return variational_plots.plot(self,*args)

34
GPy/examples/dimensionality_reduction.py
View file

@ -15,54 +15,54 @@ def bgplvm_test_model(seed=default_seed, optimize=False, verbose=1, plot=False):
num_inducing = 5
if plot:
output_dim = 1
input_dim = 2
input_dim = 3
else:
input_dim = 2
input_dim = 1
output_dim = 25
# generate GPLVM-like data
X = _np.random.rand(num_inputs, input_dim)
lengthscales = _np.random.rand(input_dim)
k = (GPy.kern.rbf(input_dim, .5, lengthscales, ARD=True)
+ GPy.kern.white(input_dim, 0.01))
#+ GPy.kern.white(input_dim, 0.01)
)
K = k.K(X)
Y = _np.random.multivariate_normal(_np.zeros(num_inputs), K, output_dim).T
lik = Gaussian(Y, normalize=True)
k = GPy.kern.rbf_inv(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim)
# k = GPy.kern.linear(input_dim) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
# k = GPy.kern.rbf_inv(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim)
k = GPy.kern.linear(input_dim)# + GPy.kern.bias(input_dim) + GPy.kern.white(input_dim, 0.00001)
# k = GPy.kern.rbf(input_dim, ARD = False) + GPy.kern.white(input_dim, 0.00001)
# k = GPy.kern.rbf(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.rbf(input_dim, .3, _np.ones(input_dim) * .2, ARD=True)
# k = GPy.kern.rbf(input_dim, .5, 2., ARD=0) + GPy.kern.rbf(input_dim, .3, .2, ARD=0)
# k = GPy.kern.rbf(input_dim, .5, _np.ones(input_dim) * 2., ARD=True) + GPy.kern.linear(input_dim, _np.ones(input_dim) * .2, ARD=True)
m = GPy.models.BayesianGPLVM(lik, input_dim, kernel=k, num_inducing=num_inducing)
m = GPy.models.BayesianGPLVM(Y, input_dim, kernel=k, num_inducing=num_inducing)
#===========================================================================
# randomly obstruct data with percentage p
p = .8
Y_obstruct = Y.copy()
Y_obstruct[_np.random.uniform(size=(Y.shape)) < p] = _np.nan
#===========================================================================
m2 = GPy.models.BayesianGPLVMWithMissingData(Y_obstruct, input_dim, kernel=k, num_inducing=num_inducing)
#m2 = GPy.models.BayesianGPLVMWithMissingData(Y_obstruct, input_dim, kernel=k, num_inducing=num_inducing)
m.lengthscales = lengthscales
if plot:
import matplotlib.pyplot as pb
m.plot()
pb.title('PCA initialisation')
m2.plot()
pb.title('PCA initialisation')
#m2.plot()
#pb.title('PCA initialisation')
if optimize:
m.optimize('scg', messages=verbose)
m2.optimize('scg', messages=verbose)
#m2.optimize('scg', messages=verbose)
if plot:
m.plot()
pb.title('After optimisation')
m2.plot()
pb.title('After optimisation')
#m2.plot()
#pb.title('After optimisation')
return m, m2
return m
def gplvm_oil_100(optimize=True, verbose=1, plot=True):
import GPy
@ -264,16 +264,16 @@ def bgplvm_simulation(optimize=True, verbose=1,
D1, D2, D3, N, num_inducing, Q = 15, 5, 8, 30, 3, 10
_, _, Ylist = _simulate_sincos(D1, D2, D3, N, num_inducing, Q, plot_sim)
Y = Ylist[0]
k = kern.linear(Q, ARD=True) + kern.bias(Q, _np.exp(-2)) + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
k = kern.linear(Q, ARD=True)# + kern.white(Q, _np.exp(-2)) # + kern.bias(Q)
m = BayesianGPLVM(Y, Q, init="PCA", num_inducing=num_inducing, kernel=k)
m['noise'] = Y.var() / 100.
m.Gaussian_noise = Y.var() / 100.
if optimize:
print "Optimizing model:"
m.optimize('scg', messages=verbose, max_iters=max_iters,
gtol=.05)
if plot:
m.plot_X_1d("BGPLVM Latent Space 1D")
m.q.plot("BGPLVM Latent Space 1D")
m.kern.plot_ARD('BGPLVM Simulation ARD Parameters')
return m

8
GPy/inference/latent_function_inference/fitc.py
View file

@ -118,8 +118,8 @@ class FITC(SparseGP):
_dKmm = .5*(V_n**2 + alpha_n + gamma_n**2 - 2.*gamma_k) * K_pp_K #Diag_dD_dKmm
self._dpsi1_dtheta += self.kern.dK_dtheta(_dpsi1,self.X[i:i+1,:],self.Z)
self._dKmm_dtheta += self.kern.dK_dtheta(_dKmm,self.Z)
self._dKmm_dX += self.kern.dK_dX(_dKmm ,self.Z)
self._dpsi1_dX += self.kern.dK_dX(_dpsi1.T,self.Z,self.X[i:i+1,:])
self._dKmm_dX += self.kern.gradients_X(_dKmm ,self.Z)
self._dpsi1_dX += self.kern.gradients_X(_dpsi1.T,self.Z,self.X[i:i+1,:])
# the partial derivative vector for the likelihood
if self.likelihood.num_params == 0:
@ -170,8 +170,8 @@ class FITC(SparseGP):
return dL_dtheta
def dL_dZ(self):
dL_dZ = self.kern.dK_dX(self._dL_dpsi1.T,self.Z,self.X)
dL_dZ += self.kern.dK_dX(self._dL_dKmm,X=self.Z)
dL_dZ = self.kern.gradients_X(self._dL_dpsi1.T,self.Z,self.X)
dL_dZ += self.kern.gradients_X(self._dL_dKmm,X=self.Z)
dL_dZ += self._dpsi1_dX
dL_dZ += self._dKmm_dX
return dL_dZ

2
GPy/inference/latent_function_inference/varDTC.py
View file

@ -80,7 +80,7 @@ class VarDTC(object):
# no backsubstitution because of bound explosion on tr(A) if not...
LmInv, _ = dtrtri(Lm, lower=1)
A = LmInv.T.dot(psi2_beta.dot(LmInv))
print A.sum()
#print A.sum()
else:
if het_noise:
tmp = psi1 * (np.sqrt(beta.reshape(num_data, 1)))

11
GPy/kern/kern.py
View file

@ -160,6 +160,9 @@ class kern(Parameterized):
return newkern
def __call__(self, X, X2=None):
return self.K(X, X2)
def __mul__(self, other):
""" Here we overload the '*' operator. See self.prod for more information"""
return self.prod(other)
@ -550,7 +553,7 @@ class Kern_check_dK_dX(Kern_check_model):
Kern_check_model.__init__(self,kernel=kernel,dL_dK=dL_dK, X=X, X2=X2)
def _log_likelihood_gradients(self):
return self.kernel.dK_dX(self.dL_dK, self.X, self.X2).flatten()
return self.kernel.gradients_X(self.dL_dK, self.X, self.X2).flatten()
def _get_param_names(self):
return ['X_' +str(i) + ','+str(j) for j in range(self.X.shape[1]) for i in range(self.X.shape[0])]
@ -657,7 +660,7 @@ def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False):
except NotImplementedError:
result=True
if verbose:
print("dK_dX not implemented for " + kern.name)
print("gradients_X not implemented for " + kern.name)
if result and verbose:
print("Check passed.")
if not result:
@ -673,7 +676,7 @@ def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False):
except NotImplementedError:
result=True
if verbose:
print("dK_dX not implemented for " + kern.name)
print("gradients_X not implemented for " + kern.name)
if result and verbose:
print("Check passed.")
if not result:
@ -689,7 +692,7 @@ def kern_test(kern, X=None, X2=None, output_ind=None, verbose=False):
except NotImplementedError:
result=True
if verbose:
print("dK_dX not implemented for " + kern.name)
print("gradients_X not implemented for " + kern.name)
if result and verbose:
print("Check passed.")
if not result:

2
GPy/kern/parts/Brownian.py
View file

@ -51,7 +51,7 @@ class Brownian(Kernpart):
def dKdiag_dtheta(self,dL_dKdiag,X,target):
target += np.dot(X.flatten(), dL_dKdiag)
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
raise NotImplementedError, "TODO"
#target += self.variance
#target -= self.variance*theta(X-X2.T)

6
GPy/kern/parts/Matern32.py
View file

@ -96,7 +96,7 @@ class Matern32(Kernpart):
"""derivative of the diagonal of the covariance matrix with respect to the parameters."""
target[0] += np.sum(dL_dKdiag)
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
"""derivative of the covariance matrix with respect to X."""
if X2 is None:
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X[None, :, :]) / self.lengthscale), -1))[:, :, None]
@ -105,8 +105,8 @@ class Matern32(Kernpart):
else:
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X2[None, :, :]) / self.lengthscale), -1))[:, :, None]
ddist_dX = (X[:, None, :] - X2[None, :, :]) / self.lengthscale ** 2 / np.where(dist != 0., dist, np.inf)
dK_dX = -np.transpose(3 * self.variance * dist * np.exp(-np.sqrt(3) * dist) * ddist_dX, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
gradients_X = -np.transpose(3 * self.variance * dist * np.exp(-np.sqrt(3) * dist) * ddist_dX, (1, 0, 2))
target += np.sum(gradients_X * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self, dL_dKdiag, X, target):
pass

6
GPy/kern/parts/Matern52.py
View file

@ -96,7 +96,7 @@ class Matern52(Kernpart):
"""derivative of the diagonal of the covariance matrix with respect to the parameters."""
target[0] += np.sum(dL_dKdiag)
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
if X2 is None:
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X[None,:,:])/self.lengthscale),-1))[:,:,None]
@ -104,8 +104,8 @@ class Matern52(Kernpart):
else:
dist = np.sqrt(np.sum(np.square((X[:,None,:]-X2[None,:,:])/self.lengthscale),-1))[:,:,None]
ddist_dX = (X[:,None,:]-X2[None,:,:])/self.lengthscale**2/np.where(dist!=0.,dist,np.inf)
dK_dX = - np.transpose(self.variance*5./3*dist*(1+np.sqrt(5)*dist)*np.exp(-np.sqrt(5)*dist)*ddist_dX,(1,0,2))
target += np.sum(dK_dX*dL_dK.T[:,:,None],0)
gradients_X = - np.transpose(self.variance*5./3*dist*(1+np.sqrt(5)*dist)*np.exp(-np.sqrt(5)*dist)*ddist_dX,(1,0,2))
target += np.sum(gradients_X*dL_dK.T[:,:,None],0)
def dKdiag_dX(self,dL_dKdiag,X,target):
pass

2
GPy/kern/parts/eq_ode1.py
View file

@ -193,7 +193,7 @@ class Eq_ode1(Kernpart):
def dKdiag_dtheta(self,dL_dKdiag,index,target):
pass
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
pass
def _extract_t_indices(self, X, X2=None, dL_dK=None):

6
GPy/kern/parts/exponential.py
View file

@ -95,13 +95,13 @@ class Exponential(Kernpart):
# NB: derivative of diagonal elements wrt lengthscale is 0
target[0] += np.sum(dL_dKdiag)
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
"""derivative of the covariance matrix with respect to X."""
if X2 is None: X2 = X
dist = np.sqrt(np.sum(np.square((X[:, None, :] - X2[None, :, :]) / self.lengthscale), -1))[:, :, None]
ddist_dX = (X[:, None, :] - X2[None, :, :]) / self.lengthscale ** 2 / np.where(dist != 0., dist, np.inf)
dK_dX = -np.transpose(self.variance * np.exp(-dist) * ddist_dX, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
gradients_X = -np.transpose(self.variance * np.exp(-dist) * ddist_dX, (1, 0, 2))
target += np.sum(gradients_X * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self, dL_dKdiag, X, target):
pass

2
GPy/kern/parts/fixed.py
View file

@ -34,7 +34,7 @@ class Fixed(Kernpart):
def dK_dtheta(self, partial, X, X2, target):
target += (partial * self.fixed_K).sum()
def dK_dX(self, partial, X, X2, target):
def gradients_X(self, partial, X, X2, target):
pass
def dKdiag_dX(self, partial, X, target):

6
GPy/kern/parts/gibbs.py
View file

@ -97,7 +97,7 @@ class Gibbs(Kernpart):
target+= np.hstack([(dL_dK*self._K_dvar).sum(), gmapping])
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
"""Derivative of the covariance matrix with respect to X."""
# First account for gradients arising from presence of X in exponent.
self._K_computations(X, X2)
@ -105,8 +105,8 @@ class Gibbs(Kernpart):
_K_dist = 2*(X[:, None, :] - X[None, :, :])
else:
_K_dist = X[:, None, :] - X2[None, :, :] # don't cache this in _K_co
dK_dX = (-2.*self.variance)*np.transpose((self._K_dvar/self._w2)[:, :, None]*_K_dist, (1, 0, 2))
target += np.sum(dK_dX*dL_dK.T[:, :, None], 0)
gradients_X = (-2.*self.variance)*np.transpose((self._K_dvar/self._w2)[:, :, None]*_K_dist, (1, 0, 2))
target += np.sum(gradients_X*dL_dK.T[:, :, None], 0)
# Now account for gradients arising from presence of X in lengthscale.
self._dK_computations(dL_dK)
if X2 is None:

2
GPy/kern/parts/hetero.py
View file

@ -90,7 +90,7 @@ class Hetero(Kernpart):
"""Gradient of diagonal of covariance with respect to parameters."""
target += 2.*self.mapping.df_dtheta(dL_dKdiag[:, None]*self.mapping.f(X), X)
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
"""Derivative of the covariance matrix with respect to X."""
if X2==None or X2 is X:
dL_dKdiag = dL_dK.flat[::dL_dK.shape[0]+1]

4
GPy/kern/parts/hierarchical.py
View file

@ -55,14 +55,14 @@ class Hierarchical(Kernpart):
[[[[k.dK_dtheta(dL_dK[s,s2],X[s],X2[s2],target[p_start:p_stop]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices_, slices2_)] for k, p_start, p_stop, slices_, slices2_ in zip(self.parts, self.param_starts, self.param_stops, slices, slices2)]
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
raise NotImplementedError
#X,slices = X[:,:-1],index_to_slices(X[:,-1])
#if X2 is None:
#X2,slices2 = X,slices
#else:
#X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
#[[[self.k.dK_dX(dL_dK[s,s2],X[s],X2[s2],target[s,:-1]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
#[[[self.k.gradients_X(dL_dK[s,s2],X[s],X2[s2],target[s,:-1]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
#
def dKdiag_dX(self,dL_dKdiag,X,target):
raise NotImplementedError

4
GPy/kern/parts/independent_outputs.py
View file

@ -79,13 +79,13 @@ class IndependentOutputs(Kernpart):
[[[self.k.dK_dtheta(dL_dK[s,s2],X[s],X2[s2],target) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
X,slices = X[:,:-1],index_to_slices(X[:,-1])
if X2 is None:
X2,slices2 = X,slices
else:
X2,slices2 = X2[:,:-1],index_to_slices(X2[:,-1])
[[[self.k.dK_dX(dL_dK[s,s2],X[s],X2[s2],target[s,:-1]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
[[[self.k.gradients_X(dL_dK[s,s2],X[s],X2[s2],target[s,:-1]) for s in slices_i] for s2 in slices_j] for slices_i,slices_j in zip(slices,slices2)]
def dKdiag_dX(self,dL_dKdiag,X,target):
X,slices = X[:,:-1],index_to_slices(X[:,-1])

3
GPy/kern/parts/kernpart.py
View file

@ -20,7 +20,6 @@ class Kernpart(Parameterized):
# the number of optimisable parameters
# the name of the covariance function.
# link to parameterized objects
self._parameters_ = []
#self._X = None
def connect_input(self, X):
@ -106,7 +105,7 @@ class Kernpart(Parameterized):
raise NotImplementedError
def dpsi2_dmuS(self,dL_dpsi2,Z,mu,S,target_mu,target_S):
raise NotImplementedError
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
raise NotImplementedError
def dKdiag_dX(self, dL_dK, X, target):
raise NotImplementedError

27
GPy/kern/parts/linear.py
View file

@ -57,6 +57,27 @@ class Linear(Kernpart):
def on_input_change(self, X):
self._K_computations(X, None)
def update_gradients_variational(self, dL_dKmm, dL_dpsi0, dL_dpsi1, dL_dpsi2, mu, S, Z):
self._psi_computations(Z, mu, S)
# psi0:
tmp = dL_dpsi0[:, None] * self.mu2_S
if self.ARD: self.variances.gradient = tmp.sum(0)
else: self.variances.gradient = tmp.sum()
#psi1
self.dK_dtheta(dL_dpsi1, mu, Z, self.variances.gradient)
#from psi2
tmp = dL_dpsi2[:, :, :, None] * (self.ZAinner[:, :, None, :] * (2 * Z)[None, None, :, :])
if self.ARD: self.variances.gradient += tmp.sum(0).sum(0).sum(0)
else: self.variances.gradient += tmp.sum()
#from Kmm
self._K_computations(Z, None)
self.dK_dtheta(dL_dKmm, Z, None, self.variances.gradient)
# def _get_params(self):
# return self.variances
#
@ -107,7 +128,7 @@ class Linear(Kernpart):
else:
target += tmp.sum()
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
if X2 is None:
target += 2*(((X[None,:, :] * self.variances)) * dL_dK[:, :, None]).sum(1)
else:
@ -150,7 +171,7 @@ class Linear(Kernpart):
target_mu += (dL_dpsi1[:, :, None] * (Z * self.variances)).sum(1)
def dpsi1_dZ(self, dL_dpsi1, Z, mu, S, target):
self.dK_dX(dL_dpsi1.T, Z, mu, target)
self.gradients_X(dL_dpsi1.T, Z, mu, target)
def psi2(self, Z, mu, S, target):
self._psi_computations(Z, mu, S)
@ -182,7 +203,7 @@ class Linear(Kernpart):
def dpsi2_dmuS_new(self, dL_dpsi2, Z, mu, S, target_mu, target_S):
tmp = np.zeros((mu.shape[0], Z.shape[0]))
self.K(mu,Z,tmp)
self.dK_dX(2.*np.sum(dL_dpsi2*tmp[:,None,:],2),mu,Z,target_mu)
self.gradients_X(2.*np.sum(dL_dpsi2*tmp[:,None,:],2),mu,Z,target_mu)
Zs = Z*self.variances
Zs_sq = Zs[:,None,:]*Zs[None,:,:]

2
GPy/kern/parts/mlp.py
View file

@ -107,7 +107,7 @@ class MLP(Kernpart):
target[0] += np.sum(self._K_dvar*dL_dK)
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
"""Derivative of the covariance matrix with respect to X"""
self._K_computations(X, X2)
arg = self._K_asin_arg

2
GPy/kern/parts/poly.py
View file

@ -99,7 +99,7 @@ class POLY(Kernpart):
target[2] += base_cov_grad.sum()
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
"""Derivative of the covariance matrix with respect to X"""
self._K_computations(X, X2)
arg = self._K_poly_arg

20
GPy/kern/parts/prod.py
View file

@ -81,21 +81,21 @@ class Prod(Kernpart):
self.k1.dKdiag_dtheta(dL_dKdiag*K2,X[:,self.slice1],target[:self.k1.num_params])
self.k2.dKdiag_dtheta(dL_dKdiag*K1,X[:,self.slice2],target[self.k1.num_params:])
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
self._K_computations(X,X2)
if X2 is None:
if not isinstance(self.k1,Coregionalize) and not isinstance(self.k2,Coregionalize):
self.k1.dK_dX(dL_dK*self._K2, X[:,self.slice1], None, target[:,self.slice1])
self.k2.dK_dX(dL_dK*self._K1, X[:,self.slice2], None, target[:,self.slice2])
self.k1.gradients_X(dL_dK*self._K2, X[:,self.slice1], None, target[:,self.slice1])
self.k2.gradients_X(dL_dK*self._K1, X[:,self.slice2], None, target[:,self.slice2])
else:#if isinstance(self.k1,Coregionalize) or isinstance(self.k2,Coregionalize):
#NOTE The indices column in the inputs makes the ki.dK_dX fail when passing None instead of X[:,self.slicei]
#NOTE The indices column in the inputs makes the ki.gradients_X fail when passing None instead of X[:,self.slicei]
X2 = X
self.k1.dK_dX(2.*dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:,self.slice1])
self.k2.dK_dX(2.*dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[:,self.slice2])
self.k1.gradients_X(2.*dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:,self.slice1])
self.k2.gradients_X(2.*dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[:,self.slice2])
else:
self.k1.dK_dX(dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:,self.slice1])
self.k2.dK_dX(dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[:,self.slice2])
self.k1.gradients_X(dL_dK*self._K2, X[:,self.slice1], X2[:,self.slice1], target[:,self.slice1])
self.k2.gradients_X(dL_dK*self._K1, X[:,self.slice2], X2[:,self.slice2], target[:,self.slice2])
def dKdiag_dX(self, dL_dKdiag, X, target):
K1 = np.zeros(X.shape[0])
@ -103,8 +103,8 @@ class Prod(Kernpart):
self.k1.Kdiag(X[:,self.slice1],K1)
self.k2.Kdiag(X[:,self.slice2],K2)
self.k1.dK_dX(dL_dKdiag*K2, X[:,self.slice1], target[:,self.slice1])
self.k2.dK_dX(dL_dKdiag*K1, X[:,self.slice2], target[:,self.slice2])
self.k1.gradients_X(dL_dKdiag*K2, X[:,self.slice1], target[:,self.slice1])
self.k2.gradients_X(dL_dKdiag*K1, X[:,self.slice2], target[:,self.slice2])
def _K_computations(self,X,X2):
if not (np.array_equal(X,self._X) and np.array_equal(X2,self._X2) and np.array_equal(self._params , self._get_params())):

10
GPy/kern/parts/prod_orthogonal.py
View file

@ -67,11 +67,11 @@ class prod_orthogonal(Kernpart):
self.k1.dKdiag_dtheta(dL_dKdiag*K2,X[:,:self.k1.input_dim],target[:self.k1.num_params])
self.k2.dKdiag_dtheta(dL_dKdiag*K1,X[:,self.k1.input_dim:],target[self.k1.num_params:])
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
self._K_computations(X,X2)
self.k1.dK_dX(dL_dK*self._K2, X[:,:self.k1.input_dim], X2[:,:self.k1.input_dim], target)
self.k2.dK_dX(dL_dK*self._K1, X[:,self.k1.input_dim:], X2[:,self.k1.input_dim:], target)
self.k1.gradients_X(dL_dK*self._K2, X[:,:self.k1.input_dim], X2[:,:self.k1.input_dim], target)
self.k2.gradients_X(dL_dK*self._K1, X[:,self.k1.input_dim:], X2[:,self.k1.input_dim:], target)
def dKdiag_dX(self, dL_dKdiag, X, target):
K1 = np.zeros(X.shape[0])
@ -79,8 +79,8 @@ class prod_orthogonal(Kernpart):
self.k1.Kdiag(X[:,0:self.k1.input_dim],K1)
self.k2.Kdiag(X[:,self.k1.input_dim:],K2)
self.k1.dK_dX(dL_dKdiag*K2, X[:,:self.k1.input_dim], target)
self.k2.dK_dX(dL_dKdiag*K1, X[:,self.k1.input_dim:], target)
self.k1.gradients_X(dL_dKdiag*K2, X[:,:self.k1.input_dim], target)
self.k2.gradients_X(dL_dKdiag*K1, X[:,self.k1.input_dim:], target)
def _K_computations(self,X,X2):
if not (np.array_equal(X,self._X) and np.array_equal(X2,self._X2) and np.array_equal(self._params , self._get_params())):

2
GPy/kern/parts/rational_quadratic.py
View file

@ -68,7 +68,7 @@ class RationalQuadratic(Kernpart):
target[0] += np.sum(dL_dKdiag)
# here self.lengthscale and self.power have no influence on Kdiag so target[1:] are unchanged
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
if X2 is None:
dist2 = np.square((X-X.T)/self.lengthscale)

13
GPy/kern/parts/rbf.py
View file

@ -51,8 +51,11 @@ class RBF(Kernpart):
lengthscale = np.ones(self.input_dim)
self.variance = Param('variance', variance)
self.lengthscale = Param('lengthscale', lengthscale)
self.lengthscale.add_observer(self, self.update_lengthscale)
self.update_lengthscale(self.lengthscale)
self.add_parameters(self.variance, self.lengthscale)
self.parameters_changed() # initializes cache
@ -114,7 +117,7 @@ class RBF(Kernpart):
self._K_computations(X, Z)
self.variance.gradient += np.sum(dL_dKnm * self._K_dvar)
if self.ARD:
self.lengthscales.gradient = self._dL_dlengthscales_via_K(dL_dKnm, X, Z)
self.lengthscale.gradient = self._dL_dlengthscales_via_K(dL_dKnm, X, Z)
else:
self.lengthscale.gradient = (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKnm)
@ -123,7 +126,7 @@ class RBF(Kernpart):
self._K_computations(Z, None)
self.variance.gradient += np.sum(dL_dKmm * self._K_dvar)
if self.ARD:
self.lengthscales.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
self.lengthscale.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
else:
self.lengthscale.gradient += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKmm)
@ -157,7 +160,7 @@ class RBF(Kernpart):
self._K_computations(Z, None)
self.variance.gradient += np.sum(dL_dKmm * self._K_dvar)
if self.ARD:
self.lengthscales.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
self.lengthscale.gradient += self._dL_dlengthscales_via_K(dL_dKmm, Z, None)
else:
self.lengthscale.gradient += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dKmm)
@ -168,8 +171,8 @@ class RBF(Kernpart):
_K_dist = 2*(X[:, None, :] - X[None, :, :])
else:
_K_dist = X[:, None, :] - X2[None, :, :] # don't cache this in _K_computations because it is high memory. If this function is being called, chances are we're not in the high memory arena.
dK_dX = (-self.variance / self.lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
gradients_X = (-self.variance / self.lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(gradients_X * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self, dL_dKdiag, X, target):
pass

6
GPy/kern/parts/rbf_inv.py
View file

@ -142,14 +142,14 @@ class RBFInv(RBF):
else:
target[1] += (self.variance / self.lengthscale) * np.sum(self._K_dvar * self._K_dist2 * dL_dK) * (-self.lengthscale2)
def dK_dX(self, dL_dK, X, X2, target):
def gradients_X(self, dL_dK, X, X2, target):
self._K_computations(X, X2)
if X2 is None:
_K_dist = 2*(X[:, None, :] - X[None, :, :])
else:
_K_dist = X[:, None, :] - X2[None, :, :] # don't cache this in _K_computations because it is high memory. If this function is being called, chances are we're not in the high memory arena.
dK_dX = (-self.variance * self.inv_lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
gradients_X = (-self.variance * self.inv_lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(gradients_X * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self, dL_dKdiag, X, target):
pass

2
GPy/kern/parts/rbfcos.py
View file

@ -88,7 +88,7 @@ class RBFCos(Kernpart):
def dKdiag_dtheta(self,dL_dKdiag,X,target):
target[0] += np.sum(dL_dKdiag)
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
#TODO!!!
raise NotImplementedError

4
GPy/kern/parts/ss_rbf.py
View file

@ -144,8 +144,8 @@ class SS_RBF(Kernpart):
_K_dist = 2*(X[:, None, :] - X[None, :, :])
else:
_K_dist = X[:, None, :] - X2[None, :, :] # don't cache this in _K_computations because it is high memory. If this function is being called, chances are we're not in the high memory arena.
dK_dX = (-self.variance / self.lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(dK_dX * dL_dK.T[:, :, None], 0)
gradients_X = (-self.variance / self.lengthscale2) * np.transpose(self._K_dvar[:, :, np.newaxis] * _K_dist, (1, 0, 2))
target += np.sum(gradients_X * dL_dK.T[:, :, None], 0)
def dKdiag_dX(self, dL_dKdiag, X, target):
pass

10
GPy/kern/parts/symmetric.py
View file

@ -54,7 +54,7 @@ class Symmetric(Kernpart):
self.k.dK_dtheta(dL_dK,AX,AX2,target)
def dK_dX(self,dL_dK,X,X2,target):
def gradients_X(self,dL_dK,X,X2,target):
"""derivative of the covariance matrix with respect to X."""
AX = np.dot(X,self.transform)
if X2 is None:
@ -62,10 +62,10 @@ class Symmetric(Kernpart):
ZX2 = AX
else:
AX2 = np.dot(X2, self.transform)
self.k.dK_dX(dL_dK, X, X2, target)
self.k.dK_dX(dL_dK, AX, X2, target)
self.k.dK_dX(dL_dK, X, AX2, target)
self.k.dK_dX(dL_dK, AX ,AX2, target)
self.k.gradients_X(dL_dK, X, X2, target)
self.k.gradients_X(dL_dK, AX, X2, target)
self.k.gradients_X(dL_dK, X, AX2, target)
self.k.gradients_X(dL_dK, AX ,AX2, target)
def Kdiag(self,X,target):
"""Compute the diagonal of the covariance matrix associated to X."""

2
GPy/kern/parts/sympykern.py
View file

@ -357,7 +357,7 @@ class spkern(Kernpart):
def dKdiag_dtheta(self,partial,X,target):
self._weave_inline(self._dKdiag_dtheta_code, X, target, Z=None, partial=partial)
def dK_dX(self,partial,X,Z,target):
def gradients_X(self,partial,X,Z,target):
if Z is None:
self._weave_inline(self._dK_dX_code_X, X, target, Z, partial)
else:

2
GPy/mappings/kernel.py
View file

@ -57,4 +57,4 @@ class Kernel(Mapping):
return np.hstack((self._df_dA.flatten(), self._df_dbias))
def df_dX(self, dL_df, X):
return self.kern.dK_dX((dL_df[:, None, :]*self.A[None, :, :]).sum(2), X, self.X)
return self.kern.gradients_X((dL_df[:, None, :]*self.A[None, :, :]).sum(2), X, self.X)

55
GPy/models/bayesian_gplvm.py
View file

@ -2,7 +2,6 @@
# Licensed under the BSD 3-clause license (see LICENSE.txt)
import numpy as np
import itertools
from gplvm import GPLVM
from .. import kern
from ..core import SparseGP
@ -23,15 +22,10 @@ class BayesianGPLVM(SparseGP, GPLVM):
:type init: 'PCA'|'random'
"""
def __init__(self, likelihood_or_Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
Z=None, kernel=None, name='bayesian gplvm', **kwargs):
if type(likelihood_or_Y) is np.ndarray:
likelihood = Gaussian(likelihood_or_Y)
else:
likelihood = likelihood_or_Y
def __init__(self, Y, input_dim, X=None, X_variance=None, init='PCA', num_inducing=10,
Z=None, kernel=None, inference_method=None, likelihood=Gaussian(), name='bayesian gplvm', **kwargs):
if X == None:
X = self.initialise_latent(init, input_dim, likelihood.Y)
X = self.initialise_latent(init, input_dim, Y)
self.init = init
if X_variance is None:
@ -44,9 +38,9 @@ class BayesianGPLVM(SparseGP, GPLVM):
if kernel is None:
kernel = kern.rbf(input_dim) # + kern.white(input_dim)
SparseGP.__init__(self, X=X, likelihood=likelihood, kernel=kernel, Z=Z, X_variance=X_variance, name=name, **kwargs)
self.q = Normal(self.X, self.X_variance)
self.add_parameter(self.q, gradient=self._dbound_dmuS, index=0)
self.q = Normal(X, X_variance)
SparseGP.__init__(self, X, Y, Z, kernel, likelihood, inference_method, X_variance, name, **kwargs)
self.add_parameter(self.q, index=0)
self.ensure_default_constraints()
def _getstate(self):
@ -94,9 +88,9 @@ class BayesianGPLVM(SparseGP, GPLVM):
return dKL_dmu, dKL_dS
def dL_dmuS(self):
dL_dmu_psi0, dL_dS_psi0 = self.kern.dpsi0_dmuS(self.dL_dpsi0, self.Z, self.X, self.X_variance)
dL_dmu_psi1, dL_dS_psi1 = self.kern.dpsi1_dmuS(self.dL_dpsi1, self.Z, self.X, self.X_variance)
dL_dmu_psi2, dL_dS_psi2 = self.kern.dpsi2_dmuS(self.dL_dpsi2, self.Z, self.X, self.X_variance)
dL_dmu_psi0, dL_dS_psi0 = self.kern.dpsi0_dmuS(self.grad_dict['dL_dpsi0'], self.Z, self.X, self.X_variance)
dL_dmu_psi1, dL_dS_psi1 = self.kern.dpsi1_dmuS(self.grad_dict['dL_dpsi1'], self.Z, self.X, self.X_variance)
dL_dmu_psi2, dL_dS_psi2 = self.kern.dpsi2_dmuS(self.grad_dict['dL_dpsi2'], self.Z, self.X, self.X_variance)
dL_dmu = dL_dmu_psi0 + dL_dmu_psi1 + dL_dmu_psi2
dL_dS = dL_dS_psi0 + dL_dS_psi1 + dL_dS_psi2
@ -107,10 +101,25 @@ class BayesianGPLVM(SparseGP, GPLVM):
var_S = np.sum(self.X_variance - np.log(self.X_variance))
return 0.5 * (var_mean + var_S) - 0.5 * self.input_dim * self.num_data
def log_likelihood(self):
ll = SparseGP.log_likelihood(self)
kl = self.KL_divergence()
return ll - kl
def parameters_changed(self):
self.posterior, self._log_marginal_likelihood, self.grad_dict = self.inference_method.inference(self.kern, self.X, self.X_variance, self.Z, self.likelihood, self.Y)
self._log_marginal_likelihood -= self.KL_divergence()
#The derivative of the bound wrt the inducing inputs Z
self.Z.gradient = self.kern.gradients_X(self.grad_dict['dL_dKmm'], self.Z)
self.Z.gradient += self.kern.dpsi1_dZ(self.grad_dict['dL_dpsi1'], self.Z, self.X, self.X_variance)
self.Z.gradient += self.kern.dpsi2_dZ(self.grad_dict['dL_dpsi2'], self.Z, self.X, self.X_variance)
dL_dmu, dL_dS = self.dL_dmuS()
dKL_dmu, dKL_dS = self.dKL_dmuS()
self.q.means.gradient = dL_dmu - dKL_dmu
self.q.variances.gradient = dL_dS - dKL_dS
# def log_likelihood(self):
# ll = SparseGP.log_likelihood(self)
# kl = self.KL_divergence()
# return ll - kl
def _dbound_dmuS(self):
dKL_dmu, dKL_dS = self.dKL_dmuS()
@ -181,18 +190,18 @@ class BayesianGPLVM(SparseGP, GPLVM):
"""
dmu_dX = np.zeros_like(Xnew)
for i in range(self.Z.shape[0]):
dmu_dX += self.kern.dK_dX(self.Cpsi1Vf[i:i + 1, :], Xnew, self.Z[i:i + 1, :])
dmu_dX += self.kern.gradients_X(self.Cpsi1Vf[i:i + 1, :], Xnew, self.Z[i:i + 1, :])
return dmu_dX
def dmu_dXnew(self, Xnew):
"""
Individual gradient of prediction at Xnew w.r.t. each sample in Xnew
"""
dK_dX = np.zeros((Xnew.shape[0], self.num_inducing))
gradients_X = np.zeros((Xnew.shape[0], self.num_inducing))
ones = np.ones((1, 1))
for i in range(self.Z.shape[0]):
dK_dX[:, i] = self.kern.dK_dX(ones, Xnew, self.Z[i:i + 1, :]).sum(-1)
return np.dot(dK_dX, self.Cpsi1Vf)
gradients_X[:, i] = self.kern.gradients_X(ones, Xnew, self.Z[i:i + 1, :]).sum(-1)
return np.dot(gradients_X, self.Cpsi1Vf)
def plot_steepest_gradient_map(self, *args, ** kwargs):
"""

2
GPy/models/bcgplvm.py
View file

@ -44,7 +44,7 @@ class BCGPLVM(GPLVM):
GP._set_params(self, x[self.mapping.num_params:])
def _log_likelihood_gradients(self):
dL_df = self.kern.dK_dX(self.dL_dK, self.X)
dL_df = self.kern.gradients_X(self.dL_dK, self.X)
dL_dtheta = self.mapping.df_dtheta(dL_df, self.likelihood.Y)
return np.hstack((dL_dtheta.flatten(), GP._log_likelihood_gradients(self)))

2
GPy/models/gplvm.py
View file

@ -60,7 +60,7 @@ class GPLVM(GP):
def jacobian(self,X):
target = np.zeros((X.shape[0],X.shape[1],self.output_dim))
for i in range(self.output_dim):
target[:,:,i]=self.kern.dK_dX(np.dot(self.Ki,self.likelihood.Y[:,i])[None, :],X,self.X)
target[:,:,i]=self.kern.gradients_X(np.dot(self.Ki,self.likelihood.Y[:,i])[None, :],X,self.X)
return target
def magnification(self,X):

2
GPy/models/sparse_gplvm.py
View file

@ -52,7 +52,7 @@ class SparseGPLVM(SparseGPRegression, GPLVM):
def dL_dX(self):
dL_dX = self.kern.dKdiag_dX(self.dL_dpsi0, self.X)
dL_dX += self.kern.dK_dX(self.dL_dpsi1, self.X, self.Z)
dL_dX += self.kern.gradients_X(self.dL_dpsi1, self.X, self.Z)
return dL_dX

3
GPy/plotting/matplot_dep/variational_plots.py
View file

@ -1,4 +1,5 @@
import pylab as pb
import pylab as pb, numpy as np
from ...util.misc import param_to_array
def plot(parameterized, fignum=None, ax=None, colors=None):
"""

5
GPy/util/caching.py
View file

@ -1,10 +1,9 @@
import numpy as np
from ..core.parameterization.array_core import ObservableArray
from ..core.parameterization.array_core import ObservableArray, ParamList
class Cacher(object):
def __init__(self, operation, limit=5):
self.limit = int(limit)
self.operation=operation
self.cached_inputs = []
self.cached_inputs = ParamList([])
self.cached_outputs = []
self.inputs_changed = []