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d280260a46
@ -202,6 +202,24 @@ class Executor:
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elif color_estimate < 0:
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elif color_estimate < 0:
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return utils.WHITE
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return utils.WHITE
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def _add_nearby_stones(self, neighbor_vertex_set, start_vertex_x, start_vertex_y, x_diff, y_diff, num_step):
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'''
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add the nearby stones around the input vertex
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:param neighbor_vertex_set: input list
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:param start_vertex_x: x axis of the input vertex
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:param start_vertex_y: y axis of the input vertex
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:param x_diff: add x axis
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:param y_diff: add y axis
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:param num_step: number of steps to be added
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:return:
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'''
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for step in xrange(num_step):
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new_neighbor_vertex = (start_vertex_x, start_vertex_y)
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if self._in_board(new_neighbor_vertex):
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neighbor_vertex_set.append((start_vertex_x, start_vertex_y))
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start_vertex_x += x_diff
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start_vertex_y += y_diff
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@ -246,7 +246,7 @@ class strategy(object):
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self.simulator.history = copy.copy(history)
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self.simulator.history = copy.copy(history)
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self.simulator.board = copy.copy(history[-1])
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self.simulator.board = copy.copy(history[-1])
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state = self.data_process(self.simulator.history, color)
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state = self.data_process(self.simulator.history, color)
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mcts = MCTS(self.simulator, self.evaluator, state, self.simulator.size ** 2 + 1, inverse=True, max_step=100)
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mcts = MCTS(self.simulator, self.evaluator, state, self.simulator.size ** 2 + 1, inverse=True, max_step=10)
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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.simulator.size ** 2 + 1, 1, p=prob).tolist()[0]
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choice = np.random.choice(self.simulator.size ** 2 + 1, 1, p=prob).tolist()[0]
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