def _preprocess_parameters_for_log_prob(self, x, **parameters):
parameters_and_data = parameters
parameters_and_data.update({"x_data": x})
number_samples, number_datapoints = get_number_samples_and_datapoints(parameters_and_data)
parameters_and_data = map_iterable(lambda y: broadcast_and_reshape_parent_value(y, number_samples, number_datapoints), parameters_and_data)
vector_names = self.vector_parameters
vector_names.add("x_data")
reshaped_parameters_and_data, _ = self._preproces_vector_input(parameters_and_data, vector_names)
x = reshaped_parameters_and_data.pop("x_data")
return x, reshaped_parameters_and_data, number_samples, number_datapoints
def _preprocess_parameters_for_sampling(self, **parameters):
number_samples, number_datapoints = get_number_samples_and_datapoints(
parameters)
parameters = map_iterable(
lambda x: broadcast_and_reshape_parent_value(
x, number_samples, number_datapoints), parameters)
reshaped_parameters, tensor_shape = self._preproces_vector_input(
parameters, self.vector_parameters)
shape = tuple([number_samples, number_datapoints] + tensor_shape)
return reshaped_parameters, shape
def _apply_link(self, parents_values):
number_samples, number_datapoints = get_number_samples_and_datapoints(parents_values)
cont_values, discrete_values = split_dict(parents_values,
condition=lambda key, val: not is_discrete(val) or contains_tensors(val))
reshaped_dict = discrete_values
if cont_values:
reshaped_dict.update(map_iterable(lambda x: broadcast_and_reshape_parent_value(x, number_samples, number_datapoints),
cont_values, recursive=True))
reshaped_output = self.link(reshaped_dict)
cast_to_new_shape = lambda tensor: tensor.view(size=(number_samples, number_datapoints) + tensor.shape[1:])
output = {key: cast_to_new_shape(val)
if is_tensor(val) else map_iterable(cast_to_new_shape, val) if contains_tensors(val) else val
for key, val in reshaped_output.items()}
return output