def three_var_aevb_groups(parametric_grouped_approxes, three_var_model, aevb_initial):
dsize = np.prod(pymc3.util.get_transformed(three_var_model.one).dshape[1:])
cls, kw = parametric_grouped_approxes
spec = cls.get_param_spec_for(d=dsize, **kw)
params = dict()
for k, v in spec.items():
if isinstance(k, int):
params[k] = dict()
for k_i, v_i in v.items():
params[k][k_i] = aevb_initial.dot(np.random.rand(7, *v_i).astype('float32'))
else:
params[k] = aevb_initial.dot(np.random.rand(7, *v).astype('float32'))
aevb_g = cls([three_var_model.one], params=params, model=three_var_model, local=True)
return [aevb_g, MeanFieldGroup(None, model=three_var_model)]
def three_var_aevb_groups(parametric_grouped_approxes, three_var_model, aevb_initial):
one_initial_value = three_var_model.initial_point[three_var_model.one.tag.value_var.name]
dsize = np.prod(one_initial_value.shape[1:])
cls, kw = parametric_grouped_approxes
spec = cls.get_param_spec_for(d=dsize, **kw)
params = dict()
for k, v in spec.items():
if isinstance(k, int):
params[k] = dict()
for k_i, v_i in v.items():
params[k][k_i] = aevb_initial.dot(np.random.rand(7, *v_i).astype("float32"))
else:
params[k] = aevb_initial.dot(np.random.rand(7, *v).astype("float32"))
aevb_g = cls([three_var_model.one], params=params, model=three_var_model, local=True)
return [aevb_g, MeanFieldGroup(None, model=three_var_model)]