def merge(self):
if not self._is_already_merged:
if len(self._unique_states_matrix_ids) > 1:
if not self._is_row_struct:
convert_to_1d_arrays = map(lambda xx: xx._as_1d_array, self)
len_of_1d_arrays = map(len, convert_to_1d_arrays)
sector_positions = np.cumsum([0] + len_of_1d_arrays)
sector_positions_slices = map(lambda xx: slice(*xx), list(tools.ngram(sector_positions, 2)))
join_all_states_arrays = np.concatenate(tuple(convert_to_1d_arrays), axis=0)
u, i = np.unique(join_all_states_arrays, return_inverse=True)
self._idx_trans_vectors = map(lambda xx: IndexTransformVector(i[xx]), sector_positions_slices)
self._new_states_matrix = StatesMatrix(data=u, eval_cls=self._eval_cls)
# TODO: self._is_row_struct == True
else:
self._is_already_merged = True
self._is_already_updated = True
pass
else:
# Case of len(self._unique_states_matrix_ids) <= 1,
# it means states_mats has only one kind of states matrix
# in this case, we don't need to do anything about merge or update
self._is_already_merged = True
self._is_already_updated = True
return self
def merge(self):
if not self._executed_merge:
if len(self._unique_states_array_ids) > 1:
states_array_lens = map(lambda xx:len(xx._states_array),self)
sector_position = map(lambda xx:slice(*xx),list(tools.ngram(np.cumsum([0] + states_array_lens),2)))
join_all_states_arrays = np.concatenate(tuple(map(lambda xx:xx._states_array,self)),axis=0)
u,i = np.unique(join_all_states_arrays,return_inverse=True)
ptrs_transforms = map(lambda xx:i[xx],sector_position)
self._new_states_array = StatesArray(data=u, eval_cls=self._eval_cls)
self._new_states_dict = StatesDictionary(states_array=self._new_states_array)
self._ptrs_transforms = ptrs_transforms
else:
self._new_states_array = self[0]._states_array
self._new_states_dict = StatesDictionary(states_array=self._new_states_array)
self._executed_merge = True
return self
def _1d_ngram(self, n):
assert self._is_1d
ngram_results = list(tools.ngram(self.flatten(), n))
return type(self)(data=ngram_results, **self._reconstruct_kwargs)
