def flatten(self, vars=None):
"""Flattens model's input and returns:
FlatView with
* input vector variable
* replacements `input_var -> vars`
* view {variable: VarMap}
Parameters
----------
vars : list of variables or None
if None, then all model.free_RVs are used for flattening input
Returns
-------
flat_view
"""
if vars is None:
vars = self.free_RVs
order = ArrayOrdering(vars)
inputvar = tt.vector('flat_view', dtype=theano.config.floatX)
inputvar.tag.test_value = flatten_list(vars).tag.test_value
replacements = {
self.named_vars[name]: inputvar[slc].reshape(shape).astype(dtype)
for name, slc, shape, dtype in order.vmap
}
view = {vm.var: vm for vm in order.vmap}
flat_view = FlatView(inputvar, replacements, view)
return flat_view
def flatten(self, vars=None, order=None, inputvar=None):
"""Flattens model's input and returns:
FlatView with
* input vector variable
* replacements ``input_var -> vars``
* view `{variable: VarMap}`
Parameters
----------
vars : list of variables or None
if None, then all model.free_RVs are used for flattening input
order : ArrayOrdering
Optional, use predefined ordering
inputvar : tt.vector
Optional, use predefined inputvar
Returns
-------
flat_view
"""
if vars is None:
vars = self.free_RVs
if order is None:
order = ArrayOrdering(vars)
if inputvar is None:
inputvar = tt.vector('flat_view', dtype=theano.config.floatX)
if theano.config.compute_test_value != 'off':
if vars:
inputvar.tag.test_value = flatten_list(vars).tag.test_value
else:
inputvar.tag.test_value = np.asarray([], inputvar.dtype)
replacements = {self.named_vars[name]: inputvar[slc].reshape(shape).astype(dtype)
for name, slc, shape, dtype in order.vmap}
view = {vm.var: vm for vm in order.vmap}
flat_view = FlatView(inputvar, replacements, view)
return flat_view
