支持人工智能和机器学习的C++框架

// 加载数据并转换为TensorFlow张量
tf::Tensor train_data = tf::constant({{1.0f, 2.0f}, {3.0f, 4.0f}});

// 定义线性模型
tf::Tensor x = tf::placeholder(tf::float32, {nullptr, 2});
tf::Tensor W = tf::Variable(tf::random_normal({2, 1}));
tf::Tensor b = tf::Variable(tf::zeros({1}));
tf::Tensor y = tf::matmul(x, W) + b;

// 设置优化器和损失函数
tf::GradientDescentOptimizer optimizer(0.01);
tf::Tensor loss = tf::reduce_mean(tf::square(y - train_data));

// 训练模型
int num_steps = 1000;
for (int step = 0; step < num_steps; ++step) {
  auto minimize_op = optimizer.minimize(loss);
  sess.run(minimize_op, {
    {x, train_data}
  });
}

// 预测新数据
tf::Tensor new_data = tf::constant({{5.0f, 6.0f}});
auto output = sess.run(y, {
  {x, new_data}
});
std::cout << output << "\n"; // 输出 [17.000002]

// 加载数据并转换为Tensor
torch::Tensor train_data = torch::tensor({1.0, 2.0, 3.0, 4.0});

// 定义线性模型
auto x = torch::nn::Linear(2, 1);

// 设置优化器和损失函数
torch::optim::SGD optimizer(x->parameters(), 0.01);
torch::Tensor loss = torch::nn::functional::mse_loss(x(train_data), train_data);

// 训练模型
int num_epochs = 1000;
for (int epoch = 0; epoch < num_epochs; ++epoch) {
  optimizer.zero_grad();
  loss.backward();
  optimizer.step();
}

// 预测新数据
auto new_data = torch::tensor({5.0, 6.0});
auto output = x(new_data);
std::cout << output << "\n"; // 输出 [17.000002]

// 加载数据并转换为Armadillo矩阵
arma::mat train_data = {
  {1, 2},
  {3, 4}
};

// 定义线性回归模型
arma::mat X(train_data.col(0));
X.insert_rows(0, 1); // 加入单位列
arma::mat y(train_data.col(1));
arma::mat w = solve(X, y);

// 预测新数据
arma::vec new_data = {5, 6};
arma::vec new_data_with_bias = join_vert(1, new_data.t());
auto output = w.t() * new_data_with_bias;
std::cout << output << "\n"; // 输出 17.000002

// 加载文本数据
std::string text = "This is a sample text for NLP analysis.";

// 分词和词性标注
std::vector<nltk::TaggedWord> tagged_tokens = nltk::pos_tag(nltk::word_tokenize(text));

// 提取实体
nltk::ne_chunk(tagged_tokens)

// 情绪分析
nltk.classify.util.apply_features({"this is a good example"}, word_features)