def __init__(self, input_box: Box, state_box: Box, refinement_list: list):
"""
:param input_box: allowed inputs
:param state_box: The state space where the hybridization is consturcteed
:param refinement_list: List of the state dimensions that can be refined during hybridization
"""
self.state_cell = StateCell(state_box.get_range())
self.input_box = input_box
self.state_cell.refinement_list = refinement_list
self.size = 1
def intersects(self, box: ztp.Box):
state_range = box.get_range()
state_patch = self.winset.get_patch(state_range)
if state_patch is not None and np.any(state_patch > 0):
return True
return False
def is_included(self, box: ztp.Box):
state_range = box.get_range()
state_patch = self.winset.get_patch(state_range)
if state_patch is not None and np.all(state_patch > 0):
return True
return False
def compute_pre2(F, n_time_steps, X: StateCell, input_range: Box, target: Box):
winning_size = np.zeros(target.center.shape)
input_range_multi = input_range.get_range()
input_range_multi = Box(np.tile(input_range_multi, (n_time_steps, 1)))
X_new = deepcopy(X)
cell_list = [X_new]
while cell_list:
cell = cell_list.pop(0)
if cell.fully_solved():
continue
if np.all(cell.as_box().get_bounding_box_size() <
target.get_bounding_box_size() / 16):
continue
if np.all(cell.as_box().get_bounding_box_size() < winning_size / 2):
print("not worth it")
break
cell.compute_multistep_affine(F, n_time_steps, input_range)
assert isinstance(cell.multi_step_dynamics, AffineSys)
ru = cell.multi_step_dynamics.get_ru(input_range_multi)
ru_inv = copy(ru)
ru_inv.center = -ru_inv.center
pre_target_over = target.minkowski_sum(
cell.multi_step_dynamics.W).minkowski_sum(ru_inv)
if np.all(target.get_bounding_box_size() >=
cell.multi_step_dynamics.W.get_bounding_box_size()):
target_under = target.get_bounding_box()
target_under.generators = target.generators - cell.multi_step_dynamics.W.get_bounding_box(
).generators
target_under.center = target_under.center - cell.multi_step_dynamics.W.center
pre_target_under = target_under.minkowski_sum(ru_inv)
else:
pre_target_under = None
sub_list = [cell]
while sub_list:
sub_cell = sub_list.pop(0)
if np.all(
sub_cell.as_box().get_bounding_box_size() < winning_size /
2):
print("not worth it")
break
if np.all(sub_cell.as_box().get_bounding_box_size() <
target.get_bounding_box_size() / 16):
continue
assert isinstance(sub_cell.multi_step_dynamics, AffineSys)
rx = sub_cell.multi_step_dynamics.get_rx(sub_cell.as_box())
if pre_target_over.intersects(rx):
if pre_target_over.contains(rx):
if pre_target_under and pre_target_under.contains(rx):
winning_size = np.maximum(
winning_size,
sub_cell.as_box().get_bounding_box_size())
sub_cell.is_winning = True
else:
sub_cell.split_cell()
cell_list = cell_list + sub_cell.children
else:
sub_cell.split_cell()
sub_list = sub_list + sub_cell.children
else:
continue
return X_new