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Merged
merged 1 commit into from
Jun 12, 2017
Merged

trying to fix #2298 #2299

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22 changes: 13 additions & 9 deletions pymc3/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -783,13 +783,17 @@ def __call__(self, *args, **kwargs):
compilef = fastfn


def _get_scaling(total_size, data):
def _get_scaling(total_size, shape, ndim):
"""
Gets scaling constant for logp

Parameters
----------
total_size : int or list[int]
data : n-dimentional tensor
shape : shape
shape to scale
ndim : int
ndim hint

Returns
-------
Expand All @@ -798,16 +802,15 @@ def _get_scaling(total_size, data):
if total_size is None:
coef = pm.floatX(1)
elif isinstance(total_size, int):
if data.ndim >= 1:
denom = data.shape[0]
if ndim >= 1:
denom = shape[0]
else:
denom = 1
coef = pm.floatX(total_size) / pm.floatX(denom)
elif isinstance(total_size, (list, tuple)):
if not all(isinstance(i, int) for i in total_size if (i is not Ellipsis and i is not None)):
raise TypeError('Unrecognized `total_size` type, expected '
'int or list of ints, got %r' % total_size)
shape = data.shape
if Ellipsis in total_size:
sep = total_size.index(Ellipsis)
begin = total_size[:sep]
Expand All @@ -817,7 +820,7 @@ def _get_scaling(total_size, data):
else:
begin = total_size
end = []
if (len(begin) + len(end)) > data.ndim:
if (len(begin) + len(end)) > ndim:
raise ValueError('Length of `total_size` is too big, '
'number of scalings is bigger that ndim, got %r' % total_size)
elif (len(begin) + len(end)) == 0:
Expand Down Expand Up @@ -866,7 +869,7 @@ def __init__(self, type=None, owner=None, index=None, name=None,
self.logp_elemwiset = distribution.logp(self)
self.total_size = total_size
self.model = model
self.scaling = _get_scaling(total_size, self)
self.scaling = _get_scaling(total_size, self.shape, self.ndim)

incorporate_methods(source=distribution, destination=self,
methods=['random'],
Expand Down Expand Up @@ -972,7 +975,7 @@ def __init__(self, type=None, owner=None, index=None, name=None, data=None,
theano.gof.Apply(theano.compile.view_op,
inputs=[data], outputs=[self])
self.tag.test_value = theano.compile.view_op(data).tag.test_value
self.scaling = _get_scaling(total_size, data)
self.scaling = _get_scaling(total_size, data.shape, data.ndim)

def _repr_latex_(self, name=None, dist=None):
if self.distribution is None:
Expand Down Expand Up @@ -1016,6 +1019,7 @@ def __init__(self, name, data, distribution, total_size=None, model=None):
self.total_size = total_size
self.model = model
self.distribution = distribution
self.scaling = _get_scaling(total_size, self.logp_elemwiset.shape, self.logp_elemwiset.ndim)


def Deterministic(name, var, model=None):
Expand Down Expand Up @@ -1093,7 +1097,7 @@ def __init__(self, type=None, owner=None, index=None, name=None,
theano.Apply(theano.compile.view_op, inputs=[
normalRV], outputs=[self])
self.tag.test_value = normalRV.tag.test_value

self.scaling = _get_scaling(total_size, self.shape, self.ndim)
incorporate_methods(source=distribution, destination=self,
methods=['random'],
wrapper=InstanceMethod)
Expand Down