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added sparsegp with missing data

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This commit is contained in:
Max Zwiessele 2014-02-18 15:52:33 +00:00
parent e96d40b4cc
commit d0c563ff0a
19 changed files with 572 additions and 325 deletions
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141
GPy/core/parameterization/array_core.py
View file

@ -36,6 +36,8 @@ class ObservableArray(np.ndarray, Observable):
# see InfoArray.__array_finalize__ for comments
if obj is None: return
self._observers_ = getattr(obj, '_observers_', None)
def __array_wrap__(self, out_arr, context=None):
return out_arr.view(np.ndarray)
def _s_not_empty(self, s):
# this checks whether there is something picked by this slice.
@ -68,11 +70,6 @@ class ObservableArray(np.ndarray, Observable):
def copy(self, *args):
return self.__copy__(*args)
def __ror__(self, *args, **kwargs):
r = np.ndarray.__ror__(self, *args, **kwargs)
self._notify_observers()
return r
def __ilshift__(self, *args, **kwargs):
r = np.ndarray.__ilshift__(self, *args, **kwargs)
self._notify_observers()
@ -83,71 +80,19 @@ class ObservableArray(np.ndarray, Observable):
self._notify_observers()
return r
def __rrshift__(self, *args, **kwargs):
r = np.ndarray.__rrshift__(self, *args, **kwargs)
self._notify_observers()
return r
def __ixor__(self, *args, **kwargs):
r = np.ndarray.__ixor__(self, *args, **kwargs)
self._notify_observers()
return r
def __rxor__(self, *args, **kwargs):
r = np.ndarray.__rxor__(self, *args, **kwargs)
self._notify_observers()
return r
def __rdivmod__(self, *args, **kwargs):
r = np.ndarray.__rdivmod__(self, *args, **kwargs)
self._notify_observers()
return r
def __radd__(self, *args, **kwargs):
r = np.ndarray.__radd__(self, *args, **kwargs)
self._notify_observers()
return r
def __rdiv__(self, *args, **kwargs):
r = np.ndarray.__rdiv__(self, *args, **kwargs)
self._notify_observers()
return r
def __rtruediv__(self, *args, **kwargs):
r = np.ndarray.__rtruediv__(self, *args, **kwargs)
self._notify_observers()
return r
def __ipow__(self, *args, **kwargs):
r = np.ndarray.__ipow__(self, *args, **kwargs)
self._notify_observers()
return r
def __rmul__(self, *args, **kwargs):
r = np.ndarray.__rmul__(self, *args, **kwargs)
self._notify_observers()
return r
def __rpow__(self, *args, **kwargs):
r = np.ndarray.__rpow__(self, *args, **kwargs)
self._notify_observers()
return r
def __rsub__(self, *args, **kwargs):
r = np.ndarray.__rsub__(self, *args, **kwargs)
self._notify_observers()
return r
def __ifloordiv__(self, *args, **kwargs):
r = np.ndarray.__ifloordiv__(self, *args, **kwargs)
self._notify_observers()
@ -178,12 +123,6 @@ class ObservableArray(np.ndarray, Observable):
return r
def __rfloordiv__(self, *args, **kwargs):
r = np.ndarray.__rfloordiv__(self, *args, **kwargs)
self._notify_observers()
return r
def __iand__(self, *args, **kwargs):
r = np.ndarray.__iand__(self, *args, **kwargs)
self._notify_observers()
@ -208,8 +147,74 @@ class ObservableArray(np.ndarray, Observable):
return r
def __rshift__(self, *args, **kwargs):
r = np.ndarray.__rshift__(self, *args, **kwargs)
self._notify_observers()
return r
# def __rrshift__(self, *args, **kwargs):
# r = np.ndarray.__rrshift__(self, *args, **kwargs)
# self._notify_observers()
# return r
# def __ror__(self, *args, **kwargs):
# r = np.ndarray.__ror__(self, *args, **kwargs)
# self._notify_observers()
# return r
# def __rxor__(self, *args, **kwargs):
# r = np.ndarray.__rxor__(self, *args, **kwargs)
# self._notify_observers()
# return r
# def __rdivmod__(self, *args, **kwargs):
# r = np.ndarray.__rdivmod__(self, *args, **kwargs)
# self._notify_observers()
# return r
# def __radd__(self, *args, **kwargs):
# r = np.ndarray.__radd__(self, *args, **kwargs)
# self._notify_observers()
# return r
# def __rdiv__(self, *args, **kwargs):
# r = np.ndarray.__rdiv__(self, *args, **kwargs)
# self._notify_observers()
# return r
# def __rtruediv__(self, *args, **kwargs):
# r = np.ndarray.__rtruediv__(self, *args, **kwargs)
# self._notify_observers()
# return r
# def __rshift__(self, *args, **kwargs):
# r = np.ndarray.__rshift__(self, *args, **kwargs)
# self._notify_observers()
# return r
# def __rmul__(self, *args, **kwargs):
# r = np.ndarray.__rmul__(self, *args, **kwargs)
# self._notify_observers()
# return r
# def __rpow__(self, *args, **kwargs):
# r = np.ndarray.__rpow__(self, *args, **kwargs)
# self._notify_observers()
# return r
# def __rsub__(self, *args, **kwargs):
# r = np.ndarray.__rsub__(self, *args, **kwargs)
# self._notify_observers()
# return r
# def __rfloordiv__(self, *args, **kwargs):
# r = np.ndarray.__rfloordiv__(self, *args, **kwargs)
# self._notify_observers()
# return r

11
GPy/core/parameterization/param.py
View file

@ -80,8 +80,6 @@ class Param(ObservableArray, Constrainable, Gradcheckable, Indexable, Parameteri
self.constraints = getattr(obj, 'constraints', None)
self.priors = getattr(obj, 'priors', None)
#def __array_wrap__(self, out_arr, context=None):
# return out_arr.view(numpy.ndarray)
#===========================================================================
# Pickling operations
#===========================================================================
@ -335,19 +333,20 @@ class ParamConcatenation(object):
if len(params)==1: return params[0]
return ParamConcatenation(params)
def __setitem__(self, s, val, update=True):
if isinstance(val, ParamConcatenation):
val = val._vals()
ind = numpy.zeros(sum(self._param_sizes), dtype=bool); ind[s] = True;
vals = self._vals(); vals[s] = val; del val
[numpy.place(p, ind[ps], vals[ps])# and p._notify_tied_parameters()
[numpy.place(p, ind[ps], vals[ps]) and update and p._notify_parameters_changed()
for p, ps in zip(self.params, self._param_slices_)]
if update:
self.params[0]._notify_parameters_changed()
def _vals(self):
return numpy.hstack([p._get_params() for p in self.params])
#===========================================================================
# parameter operations:
#===========================================================================
def update_all_params(self):
self.params[0]._notify_parameters_changed()
for p in self.params:
p._notify_parameters_changed()
def constrain(self, constraint, warning=True):
[param.constrain(constraint, update=False) for param in self.params]