Bagaimana untuk melakukan sintesis emosi dan penjanaan emosi dalam C++?

#include <iostream>
#include <unordered_map>

// 情感词典
std::unordered_map<std::string, int> sentimentDict = {
    { "happy", 3 },
    { "sad", -2 },
    { "angry", -3 },
    // 其他情感词汇
};

// 情感合成函数
int sentimentSynthesis(const std::string& text) {
    int score = 0;
    
    // 按单词拆分文本
    std::string word;
    std::stringstream ss(text);
    while (ss >> word) {
        if (sentimentDict.find(word) != sentimentDict.end()) {
            score += sentimentDict[word];
        }
    }
    
    return score;
}

int main() {
    std::string text = "I feel happy and excited.";
    int score = sentimentSynthesis(text);
    
    std::cout << "Sentiment score: " << score << std::endl;
    
    return 0;
}

#include <iostream>
#include <torch/torch.h>

// 循环神经网络模型
struct LSTMModel : torch::nn::Module {
    LSTMModel(int inputSize, int hiddenSize, int outputSize)
        : lstm(torch::nn::LSTMOptions(inputSize, hiddenSize).layers(1)),
          linear(hiddenSize, outputSize) {
        register_module("lstm", lstm);
        register_module("linear", linear);
    }

    torch::Tensor forward(torch::Tensor input) {
        auto lstmOut = lstm(input);
        auto output = linear(std::get<0>(lstmOut)[-1]);
        return output;
    }

    torch::nn::LSTM lstm;
    torch::nn::Linear linear;
};

int main() {
    torch::manual_seed(1);

    // 训练数据
    std::vector<int> happySeq = { 0, 1, 2, 3 }; // 对应编码
    std::vector<int> sadSeq = { 4, 5, 6, 3 };
    std::vector<int> angrySeq = { 7, 8, 9, 3 };
    std::vector<std::vector<int>> sequences = { happySeq, sadSeq, angrySeq };

    // 情感编码与文本映射
    std::unordered_map<int, std::string> sentimentDict = {
        { 0, "I" },
        { 1, "feel" },
        { 2, "happy" },
        { 3, "." },
        { 4, "I" },
        { 5, "feel" },
        { 6, "sad" },
        { 7, "I" },
        { 8, "feel" },
        { 9, "angry" }
    };

    // 构建训练集
    std::vector<torch::Tensor> inputs, targets;
    for (const auto& seq : sequences) {
        torch::Tensor input = torch::zeros({ seq.size()-1, 1, 1 });
        torch::Tensor target = torch::zeros({ seq.size()-1 });
        for (size_t i = 0; i < seq.size() - 1; ++i) {
            input[i][0][0] = seq[i];
            target[i] = seq[i + 1];
        }
        inputs.push_back(input);
        targets.push_back(target);
    }

    // 模型参数
    int inputSize = 1;
    int hiddenSize = 16;
    int outputSize = 10;

    // 模型
    LSTMModel model(inputSize, hiddenSize, outputSize);
    torch::optim::Adam optimizer(model.parameters(), torch::optim::AdamOptions(0.01));

    // 训练
    for (int epoch = 0; epoch < 100; ++epoch) {
        for (size_t i = 0; i < inputs.size(); ++i) {
            torch::Tensor input = inputs[i];
            torch::Tensor target = targets[i];

            optimizer.zero_grad();
            torch::Tensor output = model.forward(input);
            torch::Tensor loss = torch::nn::functional::nll_loss(torch::log_softmax(output, 1).squeeze(), target);
            loss.backward();
            optimizer.step();
        }
    }

    // 生成
    torch::Tensor input = torch::zeros({ 1, 1, 1 });
    input[0][0][0] = 0; // 输入情感：happy
    std::cout << sentimentDict[0] << " ";
    for (int i = 1; i < 5; ++i) {
        torch::Tensor output = model.forward(input);
        int pred = output.argmax().item<int>();
        std::cout << sentimentDict[pred] << " ";
        input[0][0][0] = pred;
    }
    std::cout << std::endl;

    return 0;
}