import torch
-def generate_turing_sequences(N, nb_iter=5, nb_states=4, nb_symbols=2, tape_size=5):
+def generate_turing_sequences(N, nb_iter=5, nb_states=3, nb_symbols=4, tape_size=5):
next_state = torch.randint(nb_states, (N, nb_states, nb_symbols))
next_symbol = torch.randint(nb_symbols, (N, nb_states, nb_symbols))
next_move = torch.randint(3, (N, nb_states, nb_symbols))
all_n = torch.arange(N)
tape = torch.randint(nb_symbols, (N, tape_size))
- position = torch.randint(tape_size, (N,))
- state = torch.randint(nb_states, (N,))
+ # position = torch.randint(tape_size, (N,))
+ # state = torch.randint(nb_states, (N,))
+ position = torch.zeros(N, dtype=torch.int64)
+ state = torch.zeros(N, dtype=torch.int64)
result = []
for _ in range(nb_iter):
- result.append(tape)
+ result.append(tape.clone())
current_symbol = tape[all_n, position]
tape[all_n, position] = next_symbol[all_n, state, current_symbol]
position = (position + next_move[all_n, state, current_symbol] - 1) % tape_size
if __name__ == "__main__":
print("Basic check.")
- tapes = generate_turing_sequences(5)
+ tapes = generate_turing_sequences(1, nb_iter=10)
for i in range(tapes.size(1)):
- print(f"- {i:03d} ------------------------")
+ # print(f"- {i:03d} ------------------------")
# for s, h, r in zip(state, position, tape):
# print("".join([f"{x}" for x in r]))
# print(" " * h + f"^[{s}]")