def create_tensor_field(self,
partial_state_updates,
exo_proc,
keys=['policies', 'variables']):
partial_state_updates = sanitize_partial_state_updates(
partial_state_updates) # Temporary
dfs = [
self.config_proc.create_matrix_field(partial_state_updates, k)
for k in keys
]
df = pd.concat(dfs, axis=1)
for es, i in zip(exo_proc, range(len(exo_proc))):
df['es' + str(i + 1)] = es
df['m'] = df.index + 1
return df
def generate_config(
self, initial_state, partial_state_updates,
exo_proc) -> List[Tuple[List[Callable], List[Callable]]]:
def no_update_handler(bdf, sdf):
if (bdf.empty == False) and (sdf.empty == True):
bdf_values = bdf.values.tolist()
sdf_values = [[self.no_state_identity] * len(bdf_values)
for m in range(len(partial_state_updates))]
return sdf_values, bdf_values
elif (bdf.empty == True) and (sdf.empty == False):
sdf_values = sdf.values.tolist()
bdf_values = [[self.p_identity] * len(sdf_values)
for m in range(len(partial_state_updates))]
return sdf_values, bdf_values
else:
sdf_values = sdf.values.tolist()
bdf_values = bdf.values.tolist()
return sdf_values, bdf_values
def only_ep_handler(state_dict):
sdf_functions = [
lambda var_dict, sub_step, sL, s, _input, **kwargs: (k, v)
for k, v in zip(state_dict.keys(), state_dict.values())
]
sdf_values = [sdf_functions]
bdf_values = [[self.p_identity] * len(sdf_values)]
return sdf_values, bdf_values
if len(partial_state_updates) != 0:
# backwards compatibility #
partial_state_updates = sanitize_partial_state_updates(
partial_state_updates)
bdf = self.create_matrix_field(partial_state_updates, 'policies')
sdf = self.create_matrix_field(partial_state_updates, 'variables')
sdf_values, bdf_values = no_update_handler(bdf, sdf)
zipped_list = list(zip(sdf_values, bdf_values))
else:
sdf_values, bdf_values = only_ep_handler(initial_state)
zipped_list = list(zip(sdf_values, bdf_values))
return list(map(lambda x: (x[0] + exo_proc, x[1]), zipped_list))
