def predictByGreedySearch(self,
                              inputSeq,
                              maxAnswerLength=32,
                              showAttention=False,
                              figsize=(12, 6)):
        inputSeq = filter_sent(inputSeq)
        inputSeq = [
            w for w in jieba.lcut(inputSeq) if w in self.word2id.keys()
        ]

        X = seq2id(self.word2id, inputSeq)
        XLens = torch.tensor([len(X) + 1], dtype=torch.int, device=self.device)
        X = X + [eosToken]
        X = torch.tensor([X], dtype=torch.long, device=self.device)

        d = int(self.encoderRNN.bidirectional) + 1
        hidden = torch.zeros(
            (d * self.encoderRNN.numLayers, 1, self.hiddenSize),
            dtype=torch.float32,
            device=self.device)
        encoderOutput, hidden = self.encoderRNN(X, XLens, hidden)
        hidden = hidden[-d * self.decoderRNN.numLayers::2].contiguous()

        attentionArrs = []
        Y = []
        decoderInput = torch.tensor([[sosToken]],
                                    dtype=torch.long,
                                    device=self.device)
        while decoderInput.item() != eosToken and len(Y) < maxAnswerLength:
            decoderOutput, hidden, decoderAttentionWeight = self.decoderRNN(
                decoderInput, hidden, encoderOutput)
            topv, topi = decoderOutput.topk(1)
            decoderInput = topi[:, :, 0]
            attentionArrs.append(
                decoderAttentionWeight.data.cpu().numpy().reshape(1, XLens))
            Y.append(decoderInput.item())
        outputSeq = id2seq(self.id2word, Y)
        if showAttention:
            attentionArrs = np.vstack(attentionArrs)
            fig = plt.figure(figsize=figsize)
            ax = fig.add_subplot('111')
            cax = ax.matshow(attentionArrs, cmap='bone')
            fig.colorbar(cax)
            ax.set_xticklabels(['', '<SOS>'] + inputSeq)
            ax.set_yticklabels([''] + outputSeq)
            ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
            ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
            plt.show()
        return ''.join(outputSeq[:-1])
    def predictByBeamSearch(self,
                            inputSeq,
                            beamWidth=10,
                            maxAnswerLength=32,
                            alpha=0.7,
                            isRandomChoose=False,
                            allRandomChoose=False,
                            improve=True,
                            showInfo=False):
        outputSize = len(self.id2word)
        inputSeq = filter_sent(inputSeq)
        inputSeq = [
            w for w in jieba.lcut(inputSeq) if w in self.word2id.keys()
        ]

        X = seq2id(self.word2id, inputSeq)
        XLens = torch.tensor([len(X) + 1], dtype=torch.int, device=self.device)
        X = X + [eosToken]
        X = torch.tensor([X], dtype=torch.long, device=self.device)

        d = int(self.encoderRNN.bidirectional) + 1
        hidden = torch.zeros(
            (d * self.encoderRNN.numLayers, 1, self.hiddenSize),
            dtype=torch.float32,
            device=self.device)
        encoderOutput, hidden = self.encoderRNN(X, XLens, hidden)
        hidden = hidden[-d * self.decoderRNN.numLayers::2].contiguous()

        Y = np.ones([beamWidth, maxAnswerLength], dtype='int32') * eosToken
        # prob: beamWidth × 1
        prob = np.zeros([beamWidth, 1], dtype='float32')
        decoderInput = torch.tensor([[sosToken]],
                                    dtype=torch.long,
                                    device=self.device)
        # decoderOutput: 1 × 1 × outputSize; hidden: numLayers × 1 × hiddenSize
        decoderOutput, hidden, decoderAttentionWeight = self.decoderRNN(
            decoderInput, hidden, encoderOutput)
        # topv: 1 × 1 × beamWidth; topi: 1 × 1 × beamWidth
        topv, topi = decoderOutput.topk(beamWidth)
        # decoderInput: beamWidth × 1
        decoderInput = topi.view(beamWidth, 1)
        for i in range(beamWidth):
            Y[i, 0] = decoderInput[i].item()
        Y_ = Y.copy()
        prob += topv.view(beamWidth, 1).data.cpu().numpy()
        prob_ = prob.copy()
        # hidden: numLayers × beamWidth × hiddenSize
        hidden = hidden.expand(-1, beamWidth, -1).contiguous()
        localRestId = np.array([i for i in range(beamWidth)], dtype='int32')
        encoderOutput = encoderOutput.expand(
            beamWidth, -1, -1)  # => beamWidth × 1 × hiddenSize
        for i in range(1, maxAnswerLength):
            # decoderOutput: beamWidth × 1 × outputSize; hidden: numLayers × beamWidth × hiddenSize; decoderAttentionWeight: beamWidth × 1 × XSeqLen
            decoderOutput, hidden, decoderAttentionWeight = self.decoderRNN(
                decoderInput, hidden, encoderOutput)
            # topv: beamWidth × 1; topi: beamWidth × 1
            if improve:
                decoderOutput = decoderOutput.view(-1, 1)
                if allRandomChoose:
                    topv, topi = self._random_pick_k_by_prob(decoderOutput,
                                                             k=beamWidth)
                else:
                    topv, topi = decoderOutput.topk(beamWidth, dim=0)
            else:
                topv, topi = (torch.tensor(prob[localRestId],
                                           dtype=torch.float32,
                                           device=self.device).unsqueeze(2) +
                              decoderOutput).view(-1, 1).topk(beamWidth, dim=0)
            # decoderInput: beamWidth × 1
            decoderInput = topi % outputSize

            idFrom = topi.cpu().view(-1).numpy() // outputSize
            Y[localRestId, :i + 1] = np.hstack(
                [Y[localRestId[idFrom], :i],
                 decoderInput.cpu().numpy()])
            prob[localRestId] = prob[
                localRestId[idFrom]] + topv.data.cpu().numpy()
            hidden = hidden[:, idFrom, :]

            restId = (decoderInput != eosToken).cpu().view(-1)
            localRestId = localRestId[restId.numpy().astype('bool')]
            decoderInput = decoderInput[restId]
            hidden = hidden[:, restId, :]
            encoderOutput = encoderOutput[restId]
            beamWidth = len(localRestId)
            if beamWidth < 1:
                break
        lens = [
            i.index(eosToken) if eosToken in i else maxAnswerLength
            for i in Y.tolist()
        ]
        ans = [''.join(id2seq(self.id2word, i[:l])) for i, l in zip(Y, lens)]
        prob = [prob[i, 0] / np.power(lens[i], alpha) for i in range(len(ans))]
        if isRandomChoose or allRandomChoose:
            prob = [np.exp(p) for p in prob]
            prob = [p / sum(prob) for p in prob]
            if showInfo:
                for i in range(len(ans)):
                    print((ans[i], prob[i]))
            return random_pick(ans, prob)
        else:
            ansAndProb = list(zip(ans, prob))
            ansAndProb.sort(key=lambda x: x[1], reverse=True)
            if showInfo:
                for i in ansAndProb:
                    print(i)
            return ansAndProb[0][0]