def constructBinaryTree(l):
counter = 0
y = BinaryTree.Node(l)
thislevel = [y]
A = BinaryTree.BT(y)
while len(thislevel) > 0:
nextlevel = []
for i in range(len(thislevel)):
if type(thislevel[i].value) == list and len(thislevel[i].value) == 2:
k = thislevel[i]
k.LC = BinaryTree.Node(thislevel[i].value[0])
k.RC = BinaryTree.Node(thislevel[i].value[1])
A.size += 2
k.value = "z"+str(counter)
counter += 1
k.LC.parent = k
k.RC.parent = k
nextlevel.append(k.LC)
nextlevel.append(k.RC)
thislevel = nextlevel
return A
def findSubtreeAndOpt(lines):
# still under construction
# exhaust a small tree reconstruction
i = 0
while i < len(lines):
line_header = get_line_header(lines)
# constructing the Binary Tree
N = BinaryTree.Node(lines[i][0])
BT1 = BinaryTree.BT(N)
findSubTree(lines, line_header, BT1, N)
# generate dictionary of singly-used-variables
singly_used_variables = single_used_variables(lines)
# finding all the elements, with a limit
elements = BT1.findTouchableElementsLimit(5, singly_used_variables)
# original lines involved with the elements
struct = BT1.touchableElementsStructureLimit(5, singly_used_variables)
original_line = gendistinct.lineToCode(struct, len(elements))
# substitute to be compatible with the lines
substitution(original_line, lines)
# find out all the variables that only used a single time if no such variables, skip
one_time_variables = []
for e in elements:
if singly_used_variables[e] == 0:
one_time_variables.append(e)
if len(one_time_variables) == 0:
i += 1
continue
# generate all possible structures of the binary tree
alltrees = gendistinct.genStruct(len(elements))
trees = []
# looping through all the possible structures, try to optimise it
for tree in alltrees:
T = gendistinct.convertToTrees(tree, elements)
for t in T:
trees.append(t)
# eliminate isomorphic trees
gendistinct.convertDistinct(trees, elements)
# find the best tree
# criteria for best tree:
best_tree = None
best_XOR = 100
for j in range(len(trees)):
trees[j] = gendistinct.lineToCode(trees[j], len(elements))
substitution(trees[j], lines)
# if the tree is the original tree, skip it
flag = 0
for k in range(len(trees[j])):
if set(trees[j][k][1:]) == set(original_line[k][1:]):
continue
else:
flag = 1
break
if flag == 0:
continue
XOR = XOR_count(trees[j], lines)
saves = saving(trees[j], one_time_variables, lines)
XOR = XOR - saves
if best_XOR > XOR:
best_XOR = XOR
best_tree = trees[j]
if best_XOR < 0:
saves = saving(trees[j], one_time_variables, lines)
replaceLines(original_line, best_tree, lines, one_time_variables)
i -= 1
i += 1
