def measure(self, protocol: Protocol) -> float:
vocab = list(get_vocab_from_protocol(protocol))
num_symbols = len(vocab)
bow_protocol = {}
for derivation, message in protocol.items():
message_bow = [0 for _ in range(num_symbols)]
for symbol in message:
message_bow[vocab.index(symbol)] += 1
bow_protocol[derivation] = [str(symbol) for symbol in message_bow]
return super().measure(bow_protocol)
def _protocol_to_tensor(
self, protocol: Protocol
) -> Dict[Tuple[torch.LongTensor, torch.LongTensor], torch.LongTensor]:
vocab = get_vocab_from_protocol(protocol)
concept_set = set(concept for derivation in protocol.keys()
for concept in flatten_derivation(derivation))
concepts = {concept: idx for idx, concept in enumerate(concept_set)}
tensorized_protocol = {}
for derivation, message in protocol.items():
derivation = derivation_to_tensor(derivation, concepts)
message = torch.LongTensor([vocab[char] for char in message])
tensorized_protocol[derivation] = torch.nn.functional.one_hot(
message, num_classes=len(vocab)).reshape(-1)
return tensorized_protocol