Function main

examples/empirical_kernel_map_ex.cpp:117–163

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int main()
{
    std::vector<sample_type> samples;
    std::vector<double> labels;

    // Declare an instance of the kernel we will be using.  
    const kernel_type kern(0.1);

    // create a dataset with two concentric circles.  There will be 100 points on each circle.
    generate_concentric_circles(samples, labels, 100);

    empirical_kernel_map<kernel_type> ekm;


    // Here we create an empirical_kernel_map using all of our data samples as basis samples.  
    cout << "\n\nBuilding an empirical_kernel_map with " << samples.size() << " basis samples." << endl;
    ekm.load(kern, samples);
    cout << "Test the empirical_kernel_map when loaded with every sample." << endl;
    test_empirical_kernel_map(samples, labels, ekm);






    // create a new dataset with two concentric circles.  There will be 1000 points on each circle.
    generate_concentric_circles(samples, labels, 1000);
    // Rather than use all 2000 samples as basis samples we are going to use the 
    // linearly_independent_subset_finder to pick out a good basis set.  The idea behind this 
    // object is to try and find the 40 or so samples that best spans the subspace containing all the 
    // data.  
    linearly_independent_subset_finder<kernel_type> lisf(kern, 40);
    // populate lisf with samples.  We have configured it to allow at most 40 samples but this function 
    // may determine that fewer samples are necessary to form a good basis.  In this example program
    // it will select only 26.
    fill_lisf(lisf, samples);

    // Now reload the empirical_kernel_map but this time using only our small basis  
    // selected using the linearly_independent_subset_finder.
    cout << "\n\nBuilding an empirical_kernel_map with " << lisf.size() << " basis samples." << endl;
    ekm.load(lisf);
    cout << "Test the empirical_kernel_map when loaded with samples from the lisf object." << endl;
    test_empirical_kernel_map(samples, labels, ekm);


    cout << endl;
}

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