mirror of
https://github.com/SheffieldML/GPy.git
synced 2026-05-08 19:42:39 +02:00
_highest_parent_ now follows the tree, dK_dX > gradient_X, added update_grads_variational to linear, bgplvm for new framework
This commit is contained in:
Max Zwiessele
parent
87dab55fe1
commit
e0c68d5eb3
41 changed files with 269 additions and 291 deletions
|
|
@ -4,19 +4,19 @@
|
|||
import itertools
|
||||
import numpy
|
||||
from parameter_core import Constrainable, adjust_name_for_printing
|
||||
from array_core import ObservableArray
|
||||
from array_core import ObservableArray, ParamList
|
||||
|
||||
###### printing
|
||||
__constraints_name__ = "Constraint"
|
||||
__index_name__ = "Index"
|
||||
__tie_name__ = "Tied to"
|
||||
__precision__ = numpy.get_printoptions()['precision'] # numpy printing precision used, sublassing numpy ndarray after all
|
||||
__precision__ = numpy.get_printoptions()['precision'] # numpy printing precision used, sublassing numpy ndarray after all
|
||||
__print_threshold__ = 5
|
||||
######
|
||||
|
||||
class Float(numpy.float64, Constrainable):
|
||||
def __init__(self, f, base):
|
||||
super(Float,self).__init__(f)
|
||||
super(Float, self).__init__(f)
|
||||
self._base = base
|
||||
|
||||
|
||||
|
|
@ -50,7 +50,7 @@ class Param(ObservableArray, Constrainable):
|
|||
WARNING: This overrides the functionality of x==y!!!
|
||||
Use numpy.equal(x,y) for element-wise equality testing.
|
||||
"""
|
||||
__array_priority__ = 0 # Never give back Param
|
||||
__array_priority__ = 0 # Never give back Param
|
||||
_fixes_ = None
|
||||
def __new__(cls, name, input_array, *args, **kwargs):
|
||||
obj = numpy.atleast_1d(super(Param, cls).__new__(cls, input_array=input_array))
|
||||
|
|
@ -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)
|
||||
|
|
@ -94,11 +93,10 @@ class Param(ObservableArray, Constrainable):
|
|||
#===========================================================================
|
||||
def __reduce_ex__(self):
|
||||
func, args, state = super(Param, self).__reduce__()
|
||||
return func, args, (state,
|
||||
return func, args, (state,
|
||||
(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
|
||||
#===========================================================================
|
||||
|
|
@ -166,7 +161,7 @@ class Param(ObservableArray, Constrainable):
|
|||
|
||||
:param warning: print a warning for overwriting constraints.
|
||||
"""
|
||||
self._highest_parent_._fix(self,warning)
|
||||
self._highest_parent_._fix(self, warning)
|
||||
fix = constrain_fixed
|
||||
def unconstrain_fixed(self):
|
||||
"""
|
||||
|
|
@ -190,19 +185,19 @@ class Param(ObservableArray, Constrainable):
|
|||
Note: For now only one parameter can have ties, so all of a parameter
|
||||
will be removed, when re-tieing!
|
||||
"""
|
||||
#Note: this method will tie to the parameter which is the last in
|
||||
# Note: this method will tie to the parameter which is the last in
|
||||
# the chain of ties. Thus, if you tie to a tied parameter,
|
||||
# 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"
|
||||
try:
|
||||
if self._original_:
|
||||
self[:] = param
|
||||
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)[self._current_slice_] = param
|
||||
except ValueError:
|
||||
raise ValueError("Trying to tie {} with shape {} to {} with shape {}".format(self.name, self.shape, param.name, param.shape))
|
||||
|
|
@ -248,7 +243,7 @@ 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
|
||||
|
|
@ -261,7 +256,7 @@ 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:
|
||||
|
|
@ -269,12 +264,12 @@ 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
|
||||
|
|
@ -303,11 +298,11 @@ 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)
|
||||
|
|
@ -325,11 +320,11 @@ class Param(ObservableArray, Constrainable):
|
|||
continue
|
||||
if isinstance(si, slice):
|
||||
a = si.indices(self._realshape_[i])[0]
|
||||
elif isinstance(si, (list,numpy.ndarray,tuple)):
|
||||
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):
|
||||
|
|
@ -337,8 +332,8 @@ 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
|
||||
|
|
@ -351,11 +346,11 @@ 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)))
|
||||
|
|
@ -379,7 +374,7 @@ 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]
|
||||
|
|
@ -391,31 +386,31 @@ 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):
|
||||
|
|
@ -425,12 +420,12 @@ 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):
|
||||
|
|
@ -443,9 +438,9 @@ 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
|
||||
|
|
@ -458,10 +453,10 @@ 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):
|
||||
|
|
@ -472,22 +467,22 @@ 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:
|
||||
self.params.append(p)
|
||||
self._param_sizes = [p.size for p in self.params]
|
||||
startstops = numpy.cumsum([0] + self._param_sizes)
|
||||
self._param_slices_ = [slice(start, stop) for start,stop in zip(startstops, startstops[1:])]
|
||||
self._param_slices_ = [slice(start, stop) for start, stop in zip(startstops, startstops[1:])]
|
||||
#===========================================================================
|
||||
# Get/set items, enable broadcasting
|
||||
#===========================================================================
|
||||
def __getitem__(self, s):
|
||||
ind = numpy.zeros(sum(self._param_sizes), dtype=bool); ind[s] = True;
|
||||
params = [p._get_params()[ind[ps]] for p,ps in zip(self.params, self._param_slices_) if numpy.any(p._get_params()[ind[ps]])]
|
||||
if len(params)==1: return params[0]
|
||||
params = [p._get_params()[ind[ps]] for p, ps in zip(self.params, self._param_slices_) if numpy.any(p._get_params()[ind[ps]])]
|
||||
if len(params) == 1: return params[0]
|
||||
return ParamConcatenation(params)
|
||||
def __setitem__(self, s, val, update=True):
|
||||
ind = numpy.zeros(sum(self._param_sizes), dtype=bool); ind[s] = True;
|
||||
|
|
@ -535,12 +530,12 @@ class ParamConcatenation(object):
|
|||
unconstrain_bounded.__doc__ = Param.unconstrain_bounded.__doc__
|
||||
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()
|
||||
|
|
@ -552,11 +547,11 @@ class ParamConcatenation(object):
|
|||
lx = max([p._max_len_values() for p in params])
|
||||
li = max([p._max_len_index(i) for p, i in itertools.izip(params, indices)])
|
||||
lt = max([p._max_len_names(tm, __tie_name__) for p, tm in itertools.izip(params, ties_matrices)])
|
||||
strings = [p.__str__(cm, i, tm, lc, lx, li, lt) for p, cm, i, tm in itertools.izip(params,constr_matrices,indices,ties_matrices)]
|
||||
strings = [p.__str__(cm, i, tm, lc, lx, li, lt) for p, cm, i, tm in itertools.izip(params, constr_matrices, indices, ties_matrices)]
|
||||
return "\n".join(strings)
|
||||
return "\n{}\n".format(" -"+"- | -".join(['-'*l for l in [li,lx,lc,lt]])).join(strings)
|
||||
return "\n{}\n".format(" -" + "- | -".join(['-' * l for l in [li, lx, lc, lt]])).join(strings)
|
||||
def __repr__(self):
|
||||
return "\n".join(map(repr,self.params))
|
||||
return "\n".join(map(repr, self.params))
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
|
|
@ -564,12 +559,12 @@ if __name__ == '__main__':
|
|||
from GPy.core.parameterized import Parameterized
|
||||
from GPy.core.parameter import Param
|
||||
|
||||
#X = numpy.random.randn(2,3,1,5,2,4,3)
|
||||
X = numpy.random.randn(3,2)
|
||||
# X = numpy.random.randn(2,3,1,5,2,4,3)
|
||||
X = numpy.random.randn(3, 2)
|
||||
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))
|
||||
|
|
@ -577,19 +572,19 @@ if __name__ == '__main__':
|
|||
m = Parameterized()
|
||||
rbf = Parameterized(name='rbf')
|
||||
|
||||
rbf.add_parameter(p3,p4)
|
||||
m.add_parameter(p,p1,rbf)
|
||||
rbf.add_parameter(p3, p4)
|
||||
m.add_parameter(p, p1, rbf)
|
||||
|
||||
print "setting params"
|
||||
#print m.q_v[3:5,[1,4,5]]
|
||||
# print m.q_v[3:5,[1,4,5]]
|
||||
print "constraining variance"
|
||||
#m[".*variance"].constrain_positive()
|
||||
#print "constraining rbf"
|
||||
#m.rbf_l.constrain_positive()
|
||||
#m.q_variance[1,[0,5,11,19,2]].tie_to(m.rbf_v)
|
||||
#m.rbf_v.tie_to(m.rbf_l[0])
|
||||
#m.rbf_l[0].tie_to(m.rbf_l[1])
|
||||
#m.q_v.tie_to(m.rbf_v)
|
||||
# m[".*variance"].constrain_positive()
|
||||
# print "constraining rbf"
|
||||
# m.rbf_l.constrain_positive()
|
||||
# m.q_variance[1,[0,5,11,19,2]].tie_to(m.rbf_v)
|
||||
# m.rbf_v.tie_to(m.rbf_l[0])
|
||||
# m.rbf_l[0].tie_to(m.rbf_l[1])
|
||||
# m.q_v.tie_to(m.rbf_v)
|
||||
# m.rbf_l.tie_to(m.rbf_va)
|
||||
# pt = numpy.array(params._get_params_transformed())
|
||||
# ptr = numpy.random.randn(*pt.shape)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue