def argmax(self, xs):
indices = argsort_list_descent(xs)
xs = permutate_list(xs, indices, inv=False)
xs = F.transpose_sequence(xs)
score, path = F.argmax_crf1d(self.cost, xs)
path = F.transpose_sequence(path)
path = permutate_list(path, indices, inv=True)
score = F.permutate(score, indices, inv=True)
return score, path
# def argnmax(self, xs, n=10):
# cost = cuda.to_cpu(self.cost.data)
# xs = permutate_list(xs, argsort_list_descent(xs), inv=False)
# xs = [cuda.to_cpu(x.data) for x in xs]
#
# scores = []
# paths = []
#
# for _xs in xs:
# alphas = [_xs[0]]
# for x in _xs[1:]:
# alpha = np.max(alphas[-1] + cost, axis=1) + x
# alphas.append(alpha)
#
# _scores = []
# _paths = []
# _end = len(_xs) - 1
# buf = n
#
# c = queue.PriorityQueue()
# q = queue.PriorityQueue()
# x = _xs[_end]
# for i in range(x.shape[0]):
# q.put((-alphas[_end][i], -x[i], _end,
# np.random.random(), np.array([i], np.int32)))
# while not q.empty() and c.qsize() < n + buf:
# beta, score, time, r, path = q.get()
# if time == 0:
# c.put((score, r, path))
# continue
# t = time - 1
# x = _xs[t]
# for i in range(x.shape[0]):
# _trans = score - cost[i, path[-1]]
# _beta = -alphas[t][i] + _trans
# _score = _trans - x[i]
# q.put((_beta, _score, t,
# np.random.random(), np.append(path, i)))
# while not c.empty() and len(_paths) < n:
# score, r, path = c.get()
# _scores.append(-score)
# _paths.append(path[::-1])
# scores.append(_scores)
# paths.append(_paths)
#
# return scores, paths
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