turing.py

   1 #!/usr/bin/env python
   2
   3 import torch
   4
   5
   6 def generate_turing_sequences(N, nb_iter=5, nb_states=4, nb_symbols=2, tape_size=5):
   7     next_state = torch.randint(nb_states, (N, nb_states, nb_symbols))
   8     next_symbol = torch.randint(nb_symbols, (N, nb_states, nb_symbols))
   9     next_move = torch.randint(3, (N, nb_states, nb_symbols))
  10
  11     all_n = torch.arange(N)
  12
  13     tape = torch.randint(nb_symbols, (N, tape_size))
  14     position = torch.randint(tape_size, (N,))
  15     state = torch.randint(nb_states, (N,))
  16
  17     result = []
  18
  19     for _ in range(nb_iter):
  20         result.append(tape)
  21         current_symbol = tape[all_n, position]
  22         tape[all_n, position] = next_symbol[all_n, state, current_symbol]
  23         position = (position + next_move[all_n, state, current_symbol] - 1) % tape_size
  24         state = next_state[all_n, state, current_symbol]
  25
  26     result = torch.cat([x[:, None, :] for x in result], dim=1)
  27
  28     return result
  29
  30
  31 ######################################################################
  32
  33 if __name__ == "__main__":
  34     print("Basic check.")
  35
  36     tapes = generate_turing_sequences(5)
  37
  38     for i in range(tapes.size(1)):
  39         print(f"- {i:03d} ------------------------")
  40         # for s, h, r in zip(state, position, tape):
  41         # print("".join([f"{x}" for x in r]))
  42         # print(" " * h + f"^[{s}]")
  43         for r in tapes:
  44             print("".join([f"{x}" for x in r[i]]))