def create_encoded_random_tree_scipy(depth):
fillers, roles, _ = input_data()
single_role_shape = roles[0].shape
single_filler_shape = fillers[0].shape
fillers_shapes = generate_shapes(max_tree_depth=depth,
role_shape=single_role_shape,
filler_shape=single_filler_shape)
filler_len = fillers_shapes[0][1]
max_depth = len(fillers_shapes)
new_left = fillers[0]
new_right = fillers[1]
for level_index in range(1, max_depth):
left_shift_input = shift_matrix(roles[0],
filler_len,
level_index,
name=None,
mode='sparse')
right_shift_input = shift_matrix(roles[1],
filler_len,
level_index,
name=None,
mode='sparse')
joint = left_shift_input.dot(new_left) + right_shift_input.dot(
new_right)
new_left = np.append(np.zeros((filler_len, )), joint)
new_right = new_left
return new_left
def encode_model_2_tuple(model_2_tuple: Model2Tuple, encoder=None) -> np.array:
has_weigths = model_2_tuple.weight is not None
if has_weigths:
roles = np.array([
[1, 0, 0], # r_i
[0, 1, 0], # r_alpha
[0, 0, 1], # r_w
])
else:
roles = np.array([
[1, 0], # r_i
[0, 1], # r_alpha
])
filler_index, filler_alpha, filler_weight = FillerFactory.from_model_2_tuple(
model_2_tuple)
MAX_TREE_DEPTH = 2
SINGLE_ROLE_SHAPE = roles[0].shape
SINGLE_FILLER_SHAPE = filler_index.shape
fillers_shapes = generate_shapes(max_tree_depth=MAX_TREE_DEPTH,
role_shape=SINGLE_ROLE_SHAPE,
filler_shape=SINGLE_FILLER_SHAPE)
if encoder is None:
encoder = build_tree_joiner_network(roles=roles,
fillers_shapes=fillers_shapes)
if has_weigths:
fillers = np.array([filler_index, filler_alpha, filler_weight])
subtrees = (filler_index, filler_alpha, filler_weight)
else:
fillers = np.array([filler_index, filler_alpha])
subtrees = (filler_index, filler_alpha)
model_2_tuple_encoded = elementary_join(
joiner_network=encoder,
input_structure_max_shape=fillers_shapes,
basic_roles=roles,
basic_fillers=fillers,
subtrees=subtrees)
return model_2_tuple_encoded, encoder
def sum_numbers(a, b, max_depth, roles, dual_roles, fillers, number_sum_blocks):
single_role_shape = roles[0].shape
single_filler_shape = fillers[0].shape
fillers_shapes = generate_shapes(max_tree_depth=max_depth,
role_shape=single_role_shape,
filler_shape=single_filler_shape)
joiner_network = build_tree_joiner_network(roles=roles, fillers_shapes=fillers_shapes)
a_encoded = encode(a, max_depth, roles, fillers, joiner_network)
b_encoded = encode(b, max_depth, roles, fillers, joiner_network)
keras_sum_network = build_sum_network(roles, fillers, dual_roles, max_depth, number_sum_blocks=number_sum_blocks)
a_encoded = a_encoded.reshape((1, *a_encoded.shape, 1))
b_encoded = b_encoded.reshape((1, *b_encoded.shape, 1))
decremented_number, incremented_number = keras_sum_network.predict_on_batch([
a_encoded,
b_encoded
])
c = decode(decremented_number, max_depth, dual_roles, fillers)
d = decode(incremented_number, max_depth, dual_roles, fillers)
return c, d
def number_to_tree(target_number,
max_tree_depth,
fillers,
roles,
joiner_network=None):
single_role_shape = roles[0].shape
single_filler_shape = fillers[0].shape
fillers_shapes = generate_shapes(max_tree_depth=max_tree_depth,
role_shape=single_role_shape,
filler_shape=single_filler_shape)
if target_number == 0:
return prepare_input(None, fillers_shapes)
if not joiner_network:
joiner_network = build_tree_joiner_network(
roles=roles, fillers_shapes=fillers_shapes)
one = fillers[0]
for i in range(1):
# one is a result of two joins
# 1. join of filler and role_1 a.k.a zero representation
# 2. join of step 1 and role_1
one = elementary_join(joiner_network=joiner_network,
input_structure_max_shape=fillers_shapes,
basic_roles=roles,
basic_fillers=fillers,
subtrees=(None, one))
number = one
for i in range(target_number - 1):
# easy as 2 is just one join of (one+one)
# easy as 3 is just two joins: (one+one)+one
number = elementary_join(joiner_network=joiner_network,
input_structure_max_shape=fillers_shapes,
basic_roles=roles,
basic_fillers=fillers,
subtrees=(number, one))
return number
def main():
tf.compat.v1.disable_eager_execution()
"""
First use case for the structure that should be shifted left
Starting from:
root
We want to get:
root
|
A (left-child-of-root)
"""
fillers_case_1 = np.array([
[8, 0, 0], # A
[0, 15, 0], # B
[0, 0, 10], # C
])
roles_case_1 = np.array([
[10, 0], # r_0
[0, 5], # r_1
])
MAX_TREE_DEPTH = 2
SINGLE_ROLE_SHAPE = roles_case_1[0].shape
SINGLE_FILLER_SHAPE = fillers_case_1[0].shape
fillers_shapes = generate_shapes(max_tree_depth=MAX_TREE_DEPTH,
role_shape=SINGLE_ROLE_SHAPE,
filler_shape=SINGLE_FILLER_SHAPE)
keras_joiner = build_tree_joiner_network(roles=roles_case_1,
fillers_shapes=fillers_shapes)
left_subtree_placeholder = generate_input_placeholder(fillers_shapes)
left_subtree_placeholder[0] = fillers_case_1[0].reshape(
1, *SINGLE_FILLER_SHAPE)
right_subtree_placeholder = generate_input_placeholder(fillers_shapes)
fillers_joined = keras_joiner.predict_on_batch(
[*left_subtree_placeholder, *right_subtree_placeholder])
fillers_joined = fillers_joined.reshape((*fillers_joined.shape[1:], ))
print('calculated cons (A _x_ r_0)')
"""
Second use case for the structure that should be shifted left
(continued from the first case)
Starting from:
root
|
A (left-child-of-root)
We want to get:
root
|
|
A (left-child-of-left-child-of-root)
"""
tensor_repr_A_x_r_0 = extract_per_level_tensor_representation_after_shift(
fillers_joined,
max_tree_depth=MAX_TREE_DEPTH,
role_shape=SINGLE_ROLE_SHAPE,
filler_shape=SINGLE_FILLER_SHAPE)
print('split per layer')
prepared_for_shift = reshape_to_satisfy_max_depth_after_shift(
tensor_repr_A_x_r_0, MAX_TREE_DEPTH, SINGLE_ROLE_SHAPE,
SINGLE_FILLER_SHAPE)
print('reshaped for second shift')
fillers_joined_second_case = keras_joiner.predict_on_batch(
[*prepared_for_shift, *right_subtree_placeholder])
print('calculated cons (A _x_ r_0 _x_ r_0)')
fillers_joined_second_case = fillers_joined_second_case.reshape(
(*fillers_joined_second_case.shape[1:], ))
tensor_repr_A_x_r_0_x_r_0 = extract_per_level_tensor_representation_after_shift(
fillers_joined_second_case,
max_tree_depth=MAX_TREE_DEPTH,
role_shape=SINGLE_ROLE_SHAPE,
filler_shape=SINGLE_FILLER_SHAPE)
print('split per layer after second case')
"""
Third use case for the structure that should be shifted left
(continued from the second case)
Starting from:
root
|
A (left-child-of-left-child-of-root)
We want to get:
root
| \
| B (right-child-of-root)
A (left-child-of-left-child-of-root)
"""
prepared_for_shift_A_x_r_0 = reshape_to_satisfy_max_depth_after_shift(
tensor_repr_A_x_r_0, MAX_TREE_DEPTH, SINGLE_ROLE_SHAPE,
SINGLE_FILLER_SHAPE)
right_subtree_placeholder = generate_input_placeholder(fillers_shapes)
right_subtree_placeholder[0] = fillers_case_1[1].reshape(
1, *SINGLE_FILLER_SHAPE)
fillers_joined_third_case = keras_joiner.predict_on_batch(
[*prepared_for_shift_A_x_r_0, *right_subtree_placeholder])
fillers_joined_third_case = fillers_joined_third_case.reshape(
(*fillers_joined_third_case.shape[1:], ))
tensor_repr_A_x_r_0_x_r_0_B_x_r_1 = extract_per_level_tensor_representation_after_shift(
fillers_joined_third_case,
max_tree_depth=MAX_TREE_DEPTH,
role_shape=SINGLE_ROLE_SHAPE,
filler_shape=SINGLE_FILLER_SHAPE)
print('split per layer after third case')
"""
Fourth use case for the structure that should be shifted left
(continued from the third case)
Starting from:
root
| \
| B (right-child-of-root)
A (left-child-of-left-child-of-root)
We want to get:
root
| \
| \ B (right-child-of-root)
A (left-child-of-left-child-of-root) C (right-child-of-left-child-of-root)
"""
left_subtree_placeholder = generate_input_placeholder(fillers_shapes)
right_subtree_placeholder = generate_input_placeholder(fillers_shapes)
right_subtree_placeholder[0] = fillers_case_1[2].reshape(
1, *SINGLE_FILLER_SHAPE)
fillers_joined_fourth_case_simple_c = keras_joiner.predict_on_batch(
[*left_subtree_placeholder, *right_subtree_placeholder])
fillers_joined_fourth_case_simple_c = fillers_joined_fourth_case_simple_c.reshape(
(*fillers_joined_fourth_case_simple_c.shape[1:], ))
tensor_repr_C_x_r_1 = extract_per_level_tensor_representation_after_shift(
fillers_joined_fourth_case_simple_c,
max_tree_depth=MAX_TREE_DEPTH,
role_shape=SINGLE_ROLE_SHAPE,
filler_shape=SINGLE_FILLER_SHAPE)
right_subtree_placeholder = generate_input_placeholder(fillers_shapes)
prepared_for_shift_C_x_r_1 = reshape_to_satisfy_max_depth_after_shift(
tensor_repr_C_x_r_1, MAX_TREE_DEPTH, SINGLE_ROLE_SHAPE,
SINGLE_FILLER_SHAPE)
fillers_joined_fourth_case_complex_c = keras_joiner.predict_on_batch([
*prepared_for_shift_C_x_r_1,
*right_subtree_placeholder,
])
fillers_joined_fourth_case_complex_c = fillers_joined_fourth_case_complex_c.reshape(
(*fillers_joined_fourth_case_complex_c.shape[1:], ))
tensor_repr_C_x_r_1_x_r_0 = extract_per_level_tensor_representation_after_shift(
fillers_joined_fourth_case_complex_c,
max_tree_depth=MAX_TREE_DEPTH,
role_shape=SINGLE_ROLE_SHAPE,
filler_shape=SINGLE_FILLER_SHAPE)
tree_representation = sum_tensors(tensor_repr_C_x_r_1_x_r_0,
tensor_repr_A_x_r_0_x_r_0_B_x_r_1)
print('calculated tree representation')
prepared_for_shift_tree_representation = reshape_to_satisfy_max_depth_after_shift(
tree_representation, MAX_TREE_DEPTH + 1, SINGLE_ROLE_SHAPE,
SINGLE_FILLER_SHAPE)
return prepared_for_shift_tree_representation