如何使用dlib的LDA

Question

我想在我的训练集上安装dlib的LDA，并将转换应用于训练和测试集。我写了以下最小的例子来重现问题。如果删除使用LDA的部分，则应输出有意义的预测。

#include <iostream>
#include <vector>
#include <dlib/svm.h>

int main() {

    typedef dlib::matrix<float, 2, 1> sample_type;
    typedef dlib::radial_basis_kernel<sample_type> kernel_type;
    dlib::svm_c_trainer<kernel_type> trainer;
    trainer.set_kernel(kernel_type(0.5f));
    trainer.set_c(1.0f);

    std::vector<sample_type> samples_train;
    std::vector<float> labels_train;
    std::vector<sample_type> samples_test;
    std::vector<float> labels_test;

    sample_type sample;
    float label;

    label = -1;
    sample(0) = -1;
    sample(1) = -1;
    samples_train.push_back(sample);
    labels_train.push_back(label);

    label = 1;
    sample(0) = 1;
    sample(1) = 1;
    samples_train.push_back(sample);
    labels_train.push_back(label);

    label = 1;
    sample(0) = 0.5;
    sample(1) = 0.5;
    samples_test.push_back(sample);
    labels_test.push_back(label);

    // Fit LDA on training data
    dlib::matrix<sample_type> X;
    dlib::matrix<sample_type,0,1> mean;
    dlib::compute_lda_transform(X, mean, labels_train);

    // Apply LDA on train data
    for (auto &sample_train : samples_train){
        sample_train = X * sample_train;
    }

    // Apply LDA on test data
    for (auto &sample_test : samples_test){
        sample_test = X * sample_test;
    }

    auto predictor = trainer.train(samples_train, labels_train);

    std::cout << "Train Sample 1: " << predictor(samples_train[0]) << ", label: " << labels_train[0] << std::endl;
    std::cout << "Train Sample 2: " << predictor(samples_train[1]) << ", label: " << labels_train[1] << std::endl;
    std::cout << "Test Sample: " << predictor(samples_test[0]) << ", label: " << labels_test[0] << std::endl;

}

错误：

cannot convert 'labels_train' (type 'std::__debug::vector<float>') to type 'const std::__debug::vector<long unsigned int>&'

但是如果标签与样本的类型不同，则SVM会抛出错误。我在dlib的github存储库中找不到任何示例。