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parameters have a update hirarchy, in which updates to parameters are hirarchically pursuit
This commit is contained in:
Max Zwiessele
parent
7f05dd4a43
commit
d12f055115
2 changed files with 595 additions and 564 deletions
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@ -6,6 +6,7 @@ Created on 4 Sep 2013
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import itertools
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import numpy
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from transformations import Logexp, NegativeLogexp, Logistic
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from GPy.util.misc import fast_array_equal
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###### printing
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__constraints_name__ = "Constraint"
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@ -44,11 +45,13 @@ class Param(numpy.ndarray):
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obj._name_ = name
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obj._parent_ = None
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obj._parent_index_ = None
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obj._updates_parent_ = None
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obj._gradient_ = gradient
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obj._current_slice_ = (slice(obj.shape[0]),)
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obj._realshape_ = obj.shape
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obj._realsize_ = obj.size
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obj._realndim_ = obj.ndim
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obj._updated_ = False
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from index_operations import ParamDict
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obj._tied_to_me_ = ParamDict(set)
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obj._tied_to_ = []
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@ -67,7 +70,10 @@ class Param(numpy.ndarray):
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self._realshape_ = getattr(obj, '_realshape_', None)
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self._realsize_ = getattr(obj, '_realsize_', None)
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self._realndim_ = getattr(obj, '_realndim_', None)
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self._updated_ = getattr(obj, '_updated_', None)
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self._original_ = getattr(obj, '_original_', None)
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self._updates_parent_ = getattr(obj, '_updates_parent_', None)
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def __array_wrap__(self, out_arr, context=None):
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return out_arr.view(numpy.ndarray)
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#===========================================================================
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@ -79,22 +85,30 @@ class Param(numpy.ndarray):
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(self._name_,
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self._parent_,
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self._parent_index_,
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self._updates_parent_,
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self._gradient_,
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self._current_slice_,
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self._realshape_,
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self._realsize_,
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self._realndim_,
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)
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self._tied_to_me_,
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self._tied_to_,
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self._updated_,
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)
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)
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def __setstate__(self, state):
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super(Param, self).__setstate__(state[0])
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state = list(state[1])
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self._updated_ = state.pop()
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self._tied_to_ = state.pop()
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self._tied_to_me_ = state.pop()
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self._realndim_ = state.pop()
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self._realsize_ = state.pop()
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self._realshape_ = state.pop()
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self._current_slice_ = state.pop()
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self._parent_index_ = state.pop()
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self._gradient_ = state.pop()
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self._updates_parent_ = state.pop()
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self._parent_index_ = state.pop()
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self._parent_ = state.pop()
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self._name_ = state.pop()
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#===========================================================================
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@ -145,7 +159,7 @@ class Param(numpy.ndarray):
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#===========================================================================
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# Constrain operations -> done
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#===========================================================================
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def constrain(self, transform, warning=True):
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def constrain(self, transform, warning=True, update=True):
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"""
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:param transform: the :py:class:`GPy.core.transformations.Transformation`
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to constrain the this parameter to.
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@ -155,10 +169,12 @@ class Param(numpy.ndarray):
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:py:class:`GPy.core.transformations.Transformation`.
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"""
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if self._original_: # this happens when indexing created a copy of the array
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self.__setitem__(slice(None), transform.initialize(self))
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self.__setitem__(slice(None), transform.initialize(self), update=False)
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else:
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self._parent_._get_original(self)[self._current_slice_] = transform.initialize(self)
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self._parent_._get_original(self).__setitem__(self._current_slice_, transform.initialize(self), update=False)
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self._parent_._add_constrain(self, transform, warning)
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if update:
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self._updates_parent_.parameters_changed()
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def constrain_positive(self, warning=True):
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"""
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:param warning: print a warning if re-constraining parameters.
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@ -236,11 +252,8 @@ class Param(numpy.ndarray):
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"""
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remove tie of this parameter to ties it was tied to.
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"""
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[t._remove_tie_listener(self) for t in self._tied_to_]
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def set_index(tied_to,untie_from):
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tied_to._current_slice_ = numpy.array(set(tied_to._raveled_index()) & set(untie_from._raveled_index()))
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return tied_to
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self._tied_to_ = [tied_to for tied_to in self._tied_to_ for untie_from in ties if tied_to._parent_index_ == untie_from._parent_index_ and set_index(tied_to)._current_slice_.size > 0]
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[self._parent_._get_original(t)._remove_tie_listener(self) for t in self._tied_to_]
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self._tied_to_ = [tied_to for tied_to in self._tied_to_ for t in tied_to._tied_to_me_ if self._parent_index_==self._parent_._get_original(t)._parent_index_]
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self._parent_._set_unfixed(self)
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# self._parent_._remove_tie(self, *params)
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def _fire_changed(self):
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@ -250,17 +263,25 @@ class Param(numpy.ndarray):
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self._tied_to_me_[tied_to_me] |= set(self._raveled_index())
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def _remove_tie_listener(self, to_remove):
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for t in self._tied_to_me_.keys():
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if t._parent_index_ == self._parent_index_:
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self._tied_to_me_[t] &= set(t._raveled_index())
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if len(self._tied_to_me_[t]) == 0:
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if t._parent_index_ == to_remove._parent_index_:
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new_index = list(set(t._raveled_index()) - set(to_remove._raveled_index()))
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if new_index:
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tmp = self._parent_._get_original(t)[numpy.unravel_index(new_index,t._realshape_)]
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self._tied_to_me_[tmp] = self._tied_to_me_[t]
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del self._tied_to_me_[t]
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if len(self._tied_to_me_[tmp]) == 0:
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del self._tied_to_me_[tmp]
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else:
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del self._tied_to_me_[t]
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def _on_change(self, val, ind):
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if not numpy.all(self==val[ind]):
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if not self._updated_: #not fast_array_equal(self, val[ind]):
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self._updated_ = True
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if self._original_:
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self[:] = val[ind]
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self.__setitem__(slice(None), val[ind], update=False)
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else: # this happens when indexing created a copy of the array
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self._parent_._get_original(self)[self._current_slice_] = val[ind]
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self._parent_._get_original(self).__setitem__(self._current_slice_, val[ind], update=False)
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self._fire_changed()
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self._updated_ = False
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#===========================================================================
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# Prior Operations
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#===========================================================================
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@ -294,10 +315,13 @@ class Param(numpy.ndarray):
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return new_arr
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def __getslice__(self, start, stop):
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return self.__getitem__(slice(start, stop))
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def __setitem__(self, *args, **kwargs):
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numpy.ndarray.__setitem__(self, *args, **kwargs)
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def __setslice__(self, start, stop, val):
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return self.__setitem__(slice(start, stop), val)
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def __setitem__(self, s, val, update=True):
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numpy.ndarray.__setitem__(self, s, val)
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self._fire_changed()
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self._parent_.parameters_changed()
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if update:
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self._updates_parent_.parameters_changed()
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#===========================================================================
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# Index Operations:
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#===========================================================================
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@ -322,7 +346,7 @@ class Param(numpy.ndarray):
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extended_realshape = numpy.cumprod((1,) + self._realshape_[:0:-1])[::-1]
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ind = self._indices(slice_index)
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if ind.ndim < 2: ind=ind[:,None]
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return numpy.apply_along_axis(lambda x: numpy.sum(extended_realshape*x), 1, ind)
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return numpy.asarray(numpy.apply_along_axis(lambda x: numpy.sum(extended_realshape*x), 1, ind), dtype=int)
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def _expand_index(self, slice_index=None):
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# this calculates the full indexing arrays from the slicing objects given by get_item for _real..._ attributes
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# it basically translates slices to their respective index arrays and turns negative indices around
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@ -428,19 +452,22 @@ class ParamConcatenation(object):
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self.params = params
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self._param_sizes = [p.size for p in self.params]
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startstops = numpy.cumsum([0] + self._param_sizes)
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self._param_slices = [slice(start, stop) for start,stop in zip(startstops, startstops[1:])]
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self._param_slices_ = [slice(start, stop) for start,stop in zip(startstops, startstops[1:])]
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#===========================================================================
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# Get/set items, enable broadcasting
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#===========================================================================
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def __getitem__(self, s):
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ind = numpy.zeros(sum(self._param_sizes), dtype=bool); ind[s] = True;
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params = [p.flatten()[ind[ps]] for p,ps in zip(self.params, self._param_slices) if numpy.any(p.flat[ind[ps]])]
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params = [p.flatten()[ind[ps]] for p,ps in zip(self.params, self._param_slices_) if numpy.any(p.flat[ind[ps]])]
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if len(params)==1: return params[0]
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return ParamConcatenation(params)
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def __setitem__(self, s, val):
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def __setitem__(self, s, val, update=True):
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ind = numpy.zeros(sum(self._param_sizes), dtype=bool); ind[s] = True;
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vals = self._vals(); vals[s] = val; del val
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[numpy.place(p, ind[ps], vals[ps]) for p, ps in zip(self.params, self._param_slices)]
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[numpy.place(p, ind[ps], vals[ps]) and p._fire_changed()
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for p, ps in zip(self.params, self._param_slices_)]
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if update:
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self.params[0]._updates_parent_.parameters_changed()
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def _vals(self):
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return numpy.hstack([p._get_params() for p in self.params])
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#===========================================================================
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@ -502,7 +529,7 @@ class ParamConcatenation(object):
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if __name__ == '__main__':
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from GPy.core.parameterized import Parameterized
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#X = numpy.random.randn(2,3,1,5,2,4,3)
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X = numpy.random.randn(100,20)
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X = numpy.random.randn(1000,20)
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print "random done"
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p = Param("q_mean", X, None)
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p1 = Param("q_variance", numpy.random.rand(*p.shape), None)
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