1-ply now works again.
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network.py
35
network.py
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@ -323,42 +323,9 @@ class Network:
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scores_means = [tf.reduce_mean(score) for score in all_scores]
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scores_means = [tf.reduce_mean(score) for score in all_scores]
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transformed_means = [tf.reduce_mean(score) for score in transformed_scores]
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transformed_means = [tf.reduce_mean(score) for score in transformed_scores]
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return ([scores_means, transformed_means])
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print(time.time() - start)
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print(time.time() - start)
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# count = 0
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return ([scores_means, transformed_means])
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# # loop over boards
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# for a_board in boards:
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# a_board_scores = []
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#
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# # loop over all rolls, for each board
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# for roll in all_rolls:
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#
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# # find all states we can get to, given the board and roll and the opposite player
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# all_rolls_boards = Board.calculate_legal_states(a_board, player*-1, roll)
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# count += len(all_rolls_boards)
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# # find scores for each board found above
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# spec_roll_scores = [self.eval_state(sess, self.board_trans_func(new_board, player*-1))
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# for new_board in all_rolls_boards]
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#
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# # if the original player is the -1 player, then we need to find (1-value)
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# spec_roll_scores = [x if player == 1 else (1-x) for x in spec_roll_scores]
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#
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# # find the best score
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# best_score = max(spec_roll_scores)
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#
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# # append the best score to a_board_scores, where we keep track of the best score for each board
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# a_board_scores.append(best_score)
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#
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# # save the expected average of board scores
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# all_rolls_scores.append(sum(a_board_scores)/len(a_board_scores))
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#
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# # return all the average scores
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# print(count)
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# return all_rolls_scores
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def calc_n_ply(self, n_init, sess, board, player, roll):
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def calc_n_ply(self, n_init, sess, board, player, roll):
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