def increment(a, max_number=None) -> Tuple[any, int]:
if max_number is None:
max_number = a + 1
max_tree_depth = get_max_tree_depth(
max_number
) # as one is already represented by structure with 2 levels
a_tree = number_to_tree(a, max_tree_depth, fillers, roles)
a_tree_flattened = flattenize_per_tensor_representation(a_tree)
keras_increment_network = build_increment_network(
roles=roles,
dual_roles=dual_basic_roles_case_1,
fillers=fillers,
max_depth=max_tree_depth)
a_tree_flattened = a_tree_flattened.reshape(
(1, *a_tree_flattened.shape, 1))
new_number = keras_increment_network.predict_on_batch(
[a_tree_flattened])
new_number = new_number.reshape((*new_number.shape[1:], ))
return (extract_per_level_tensor_representation_after_unshift(
new_number, max_tree_depth, SINGLE_ROLE_SHAPE,
SINGLE_FILLER_SHAPE), max_tree_depth)
def test_single_sum_2_0(self):
max_number = 3
max_tree_depth = get_max_tree_depth(max_number)
a = 2
b = 0
c, d = sum_numbers(a,
b,
max_tree_depth,
roles,
dual_basic_roles_case_1,
fillers,
number_sum_blocks=1)
self.assertEqual(1, c)
self.assertEqual(1, d)
def test_sum_0_3(self):
max_number = 3
max_tree_depth = get_max_tree_depth(max_number)
a = 0
b = 3
c, d = sum_numbers(a,
b,
max_tree_depth,
roles,
dual_basic_roles_case_1,
fillers,
number_sum_blocks=max_number)
self.assertEqual(0, c)
self.assertEqual(3, d)
tf.compat.v1.disable_eager_execution()
# Input information
fillers = np.array([
[7, 0, 0, 0, 0], # A
])
roles = np.array([
[10, 0], # r_0
[0, 5], # r_1
])
dual_basic_roles_case_1 = np.linalg.inv(roles)
order_case_active = ['A', ]
MAX_NUMBER = 4
MAX_TREE_DEPTH = get_max_tree_depth(MAX_NUMBER)
SINGLE_ROLE_SHAPE = roles[0].shape
SINGLE_FILLER_SHAPE = fillers[0].shape
# TPR-Inc Network
a = 0
new_number_one = number_to_tree(a, MAX_TREE_DEPTH, fillers, roles)
one_unshifted = flattenize_per_tensor_representation(new_number_one)
keras_increment_network = build_increment_network(roles=roles,
dual_roles=dual_basic_roles_case_1,
fillers=fillers,
max_depth=MAX_TREE_DEPTH)
print('Built increment network')
one_unshifted = one_unshifted.reshape((1, *one_unshifted.shape, 1))
