def get_sgf_move_probs(sgf_game, policy, player):
with open(sgf_game, "r") as f:
sgf_game = f.read()
def get_single_prob(move, move_probs):
for (mv, prob) in move_probs:
if move == mv:
return prob
return 0
return [(move, get_single_prob(move, policy.eval_state(state)))
for (state, move, pl) in sgf_iter_states(sgf_game) if pl == player]
def convert_game(self, file_name, bd_size): """Read the given SGF file into an iterable of (input,output) pairs for neural network training Each input is a GameState converted into one-hot neural net features Each output is an action as an (x,y) pair (passes are skipped) If this game's size does not match bd_size, a SizeMismatchError is raised """ with open(file_name, 'r') as file_object: state_action_iterator = sgf_iter_states(file_object.read(), include_end=False) for (state, move, player) in state_action_iterator: if state.size != bd_size: raise SizeMismatchError() if move != go.PASS_MOVE: nn_input = self.feature_processor.state_to_tensor(state) yield (nn_input, move)
def __init__(self, policy, sgf_game):
with open(sgf_game, "r") as f:
sgf_game = f.read()
self.moves = [move for (_, move, _) in sgf_iter_states(sgf_game)]
self.policy = policy
