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array core and bgplvm working > changes due to __i<op>__ will now be reported

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This commit is contained in:
Max Zwiessele 2014-02-11 16:54:33 +00:00
parent 4cfc13d5fc
commit 9d262936c6
10 changed files with 177 additions and 54 deletions
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182
GPy/core/parameterization/array_core.py
View file

@ -12,6 +12,7 @@ class ListArray(np.ndarray):
WARNING: This overrides the functionality of x==y!!!
Use numpy.equal(x,y) for element-wise equality testing.
"""
def __new__(cls, input_array):
obj = np.asanyarray(input_array).view(cls)
return obj
@ -27,24 +28,6 @@ class ParamList(list):
return False
pass
class C(np.ndarray):
__array_priority__ = 1.
def __new__(cls, array):
obj = array.view(cls)
return obj
#def __array_finalize__(self, obj):
# #print 'finalize'
# return obj
def __array_prepare__(self, out_arr, context):
#print 'prepare'
while type(out_arr) is C:
out_arr = out_arr.base
return out_arr
def __array_wrap__(self, out_arr, context):
#print 'wrap', type(self), type(out_arr), context
while type(out_arr) is C:
out_arr = out_arr.base
return out_arr
class ObservableArray(ListArray, Observable):
"""
@ -63,25 +46,168 @@ class ObservableArray(ListArray, Observable):
# see InfoArray.__array_finalize__ for comments
if obj is None: return
self._observers_ = getattr(obj, '_observers_', None)
def __setitem__(self, s, val, update=True):
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):
return self.__setitem__(slice(start, stop), val)
def __copy__(self, *args):
return ObservableArray(self.base.base.copy(*args))
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()
return r
def __irshift__(self, *args, **kwargs):
r = np.ndarray.__irshift__(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 __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()
return r
def __isub__(self, *args, **kwargs):
r = np.ndarray.__isub__(self, *args, **kwargs)
self._notify_observers()
return r
def __ior__(self, *args, **kwargs):
r = np.ndarray.__ior__(self, *args, **kwargs)
self._notify_observers()
return r
def __itruediv__(self, *args, **kwargs):
r = np.ndarray.__itruediv__(self, *args, **kwargs)
self._notify_observers()
return r
def __idiv__(self, *args, **kwargs):
r = np.ndarray.__idiv__(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
def __iand__(self, *args, **kwargs):
r = np.ndarray.__iand__(self, *args, **kwargs)
self._notify_observers()
return r
def __imod__(self, *args, **kwargs):
r = np.ndarray.__imod__(self, *args, **kwargs)
self._notify_observers()
return r
def __iadd__(self, *args, **kwargs):
r = np.ndarray.__iadd__(self, *args, **kwargs)
self._notify_observers()
return r
def __imul__(self, *args, **kwargs):
r = np.ndarray.__imul__(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

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

@ -15,9 +15,9 @@ class Normal(Parameterized):
'''
def __init__(self, means, variances, name='latent space'):
Parameterized.__init__(self, name=name)
self.means = Param("mean", means)
self.variances = Param('variance', variances, Logexp())
self.add_parameters(self.means, self.variances)
self.mean = Param("mean", means)
self.variance = Param('variance', variances, Logexp())
self.add_parameters(self.mean, self.variance)
def plot(self, *args):
"""