count the winning rate for each player
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8780417378
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1
AlphaGo/.gitignore
vendored
1
AlphaGo/.gitignore
vendored
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data
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data
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checkpoints
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checkpoints
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checkpoints_origin
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checkpoints_origin
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*.log
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29
AlphaGo/data_statistic.py
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29
AlphaGo/data_statistic.py
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import os
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import cPickle
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class Data(object):
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def __init__(self):
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self.boards = []
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self.probs = []
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self.winner = 0
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def file_to_training_data(file_name):
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with open(file_name, 'rb') as file:
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try:
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file.seek(0)
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data = cPickle.load(file)
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return data.winner
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except Exception as e:
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print(e)
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return 0
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if __name__ == "__main__":
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win_count = [0, 0, 0]
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file_list = os.listdir("./data")
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#print file_list
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for file in file_list:
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win_count[file_to_training_data("./data/" + file)] += 1
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print "Total play : " + str(len(file_list))
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print "Black wins : " + str(win_count[1])
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print "White wins : " + str(win_count[-1])
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@ -62,7 +62,7 @@ class Game:
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def think(self, latest_boards, color):
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def think(self, latest_boards, color):
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mcts = MCTS(self.game_engine, self.evaluator, [latest_boards, color], self.size ** 2 + 1, inverse=True)
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mcts = MCTS(self.game_engine, self.evaluator, [latest_boards, color], self.size ** 2 + 1, inverse=True)
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mcts.search(max_step=20)
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mcts.search(max_step=100)
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temp = 1
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temp = 1
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prob = mcts.root.N ** temp / np.sum(mcts.root.N ** temp)
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prob = mcts.root.N ** temp / np.sum(mcts.root.N ** temp)
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choice = np.random.choice(self.size ** 2 + 1, 1, p=prob).tolist()[0]
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choice = np.random.choice(self.size ** 2 + 1, 1, p=prob).tolist()[0]
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