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1-ply now works again.

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Alexander Munch-Hansen 2018-05-10 19:13:18 +02:00
parent 2d84cd5a0b
commit 1aedc23de1
1 changed files with 1 additions and 34 deletions
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35
network.py
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@ -323,42 +323,9 @@ class Network:
scores_means = [tf.reduce_mean(score) for score in all_scores]
transformed_means = [tf.reduce_mean(score) for score in transformed_scores]
return ([scores_means, transformed_means])
print(time.time() - start)
# count = 0
# # loop over boards
# for a_board in boards:
# a_board_scores = []
#
# # loop over all rolls, for each board
# for roll in all_rolls:
#
# # find all states we can get to, given the board and roll and the opposite player
# all_rolls_boards = Board.calculate_legal_states(a_board, player*-1, roll)
# count += len(all_rolls_boards)
# # find scores for each board found above
# spec_roll_scores = [self.eval_state(sess, self.board_trans_func(new_board, player*-1))
# for new_board in all_rolls_boards]
#
# # if the original player is the -1 player, then we need to find (1-value)
# spec_roll_scores = [x if player == 1 else (1-x) for x in spec_roll_scores]
#
# # find the best score
# best_score = max(spec_roll_scores)
#
# # append the best score to a_board_scores, where we keep track of the best score for each board
# a_board_scores.append(best_score)
#
# # save the expected average of board scores
# all_rolls_scores.append(sum(a_board_scores)/len(a_board_scores))
#
# # return all the average scores
# print(count)
# return all_rolls_scores
return ([scores_means, transformed_means])
def calc_n_ply(self, n_init, sess, board, player, roll):