turing.py

   1 #!/usr/bin/env python
   2
   3 import torch
   4
   5
   6 def generate_turing_sequences(N, nb_iter=5, nb_states=3, nb_symbols=4, 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     position = torch.zeros(N, dtype=torch.int64)
  17     state = torch.zeros(N, dtype=torch.int64)
  18
  19     result = []
  20
  21     for _ in range(nb_iter):
  22         result.append(tape.clone())
  23         current_symbol = tape[all_n, position]
  24         tape[all_n, position] = next_symbol[all_n, state, current_symbol]
  25         position = (position + next_move[all_n, state, current_symbol] - 1) % tape_size
  26         state = next_state[all_n, state, current_symbol]
  27
  28     result = torch.cat([x[:, None, :] for x in result], dim=1)
  29
  30     return result
  31
  32
  33 ######################################################################
  34
  35 if __name__ == "__main__":
  36     print("Basic check.")
  37
  38     tapes = generate_turing_sequences(1, nb_iter=10)
  39
  40     for i in range(tapes.size(1)):
  41         # print(f"- {i:03d} ------------------------")
  42         # for s, h, r in zip(state, position, tape):
  43         # print("".join([f"{x}" for x in r]))
  44         # print(" " * h + f"^[{s}]")
  45         for r in tapes:
  46             print("".join([f"{x}" for x in r[i]]))