def main():
# set up vtree and manager
var_count = 4
vtree_type = "right".encode()
vtree = Vtree(var_count=var_count, vtree_type=vtree_type)
manager = SddManager(vtree=vtree)
x = [None] + [manager.literal(i) for i in range(1, 5)]
# construct the term X_1 ^ X_2 ^ X_3 ^ X_4
alpha = x[1] & x[2] & x[3] & x[4]
# construct the term ~X_1 ^ X_2 ^ X_3 ^ X_4
beta = ~x[1] & x[2] & x[3] & x[4]
# construct the term ~X_1 ^ ~X_2 ^ X_3 ^ X_4
gamma = ~x[1] & ~x[2] & x[3] & x[4]
print("== before referencing:")
print(f" live sdd size = {manager.live_size()}")
print(f" dead sdd size = {manager.dead_size()}")
# ref SDDs so that they are not garbage collected
alpha.ref()
beta.ref()
gamma.ref()
print("== after referencing:")
print(f" live sdd size = {manager.live_size()}")
print(f" dead sdd size = {manager.dead_size()}")
# garbage collect
manager.garbage_collect()
print("== after garbage collection:")
print(f" live sdd size = {manager.live_size()}")
print(f" dead sdd size = {manager.dead_size()}")
alpha.deref()
beta.deref()
gamma.deref()
print("saving vtree & shared sdd ...")
if not Path("output").is_dir():
raise Exception(f"Directory 'output' does not exist")
vtree.save_as_dot("output/shared-vtree.dot".encode())
manager.shared_save_as_dot("output/shared.dot".encode())
def computeTensorWMC(node: SddNode, manager: SddManager, literal2OutputNeuron: dict,
weights: torch.tensor) -> torch.tensor:
stack = list()
nodesToTensors = dict()
stack.append(node)
while len(stack) > 0:
top = stack[len(stack) - 1]
if top not in nodesToTensors:
if top.is_decision():
noTensor = False
for element in top.elements():
if element[0] not in nodesToTensors:
stack.append(element[0])
noTensor = True
if element[1] not in nodesToTensors:
stack.append(element[1])
noTensor = True
if noTensor == False:
if useGPU:
result = torch.tensor([0.0], requires_grad=True).cuda()
else:
result = torch.tensor([0.0], requires_grad=True)
for element in top.elements():
result = result + nodesToTensors[element[0]] * nodesToTensors[element[1]]
nodesToTensors[top] = result
stack.pop()
elif top.is_literal():
literal = top.literal
if literal < 0:
positive = manager.literal(-literal)
neuronIndex = literal2OutputNeuron[positive]
nodesToTensors[top] = 1 - weights[neuronIndex]
else:
neuronIndex = literal2OutputNeuron[top]
nodesToTensors[top] = weights[neuronIndex]
stack.pop()
elif top.is_false():
if useGPU:
nodesToTensors[top] = torch.tensor([0.0], requires_grad=True).cuda()
else:
nodesToTensors[top] = torch.tensor([0.0], requires_grad=True)
stack.pop()
elif top.is_true():
if useGPU:
nodesToTensors[top] = torch.tensor([1.0], requires_grad=True).cuda()
else:
nodesToTensors[top] = torch.tensor([1.0], requires_grad=True)
stack.pop()
else:
stack.pop()
return nodesToTensors[node]
