# Pastebin ZNAys3Bc
#include <dlib/clustering.h>
#include <dlib/rand.h>
#include <mlpack/core.hpp>
#include <mlpack/core/util/timers.hpp>

using namespace mlpack;
using namespace std;
using namespace dlib;

// Information about the program itself.
PROGRAM_INFO("KMEANS Clustering",
    "This program will perform KMEANS clustering with the DLib-ml "
    "library.");

// Define our input parameters that this program will take.
PARAM_STRING_IN("reference_file", "File containing the reference dataset.",
    "r", "");
PARAM_INT_IN("k", "Value of K", "k", 0);
PARAM_STRING_IN("query_file", "File containing query points (optional).", "q", "");
PARAM_STRING_OUT("assignments_file", "File to output assignments into.", "a");

int main(int argc, char** argv)
{
  // Parse command line options.
  CLI::ParseCommandLine(argc, argv);

  // Get all the parameters.
  const string referenceFile = CLI::GetParam<string>("reference_file");
  const string queryFile = CLI::GetParam<string>("query_file");
  size_t k = CLI::GetParam<int>("k");
  arma::mat referenceData;
  arma::mat queryData; // So it doesn't go out of scope.
  data::Load(referenceFile, referenceData, true);

  Log::Info << "Loaded reference data from '" << referenceFile << "' ("
      << referenceData.n_rows << " x " << referenceData.n_cols << ")." << endl;
  
  if (queryFile != "")
  {
    data::Load(queryFile, queryData, true);
    Log::Info << "Loaded query data from '" << queryFile << "' ("
        << queryData.n_rows << " x " << queryData.n_cols << ")." << endl;
  }
  
  typedef matrix<double,referenceData.n_rows,referenceData.n_cols> sample_type;
  typedef radial_basis_kernel<sample_type> kernel_type;
  
  kcentroid<kernel_type> kc(kernel_type(0.1),0.01, 8);
  kkmeans<kernel_type> test(kc);
  
  std::vector<sample_type> samples = referenceData;
  std::vector<sample_type> initial_centers;
  
  test.set_number_of_centers(k);
  
  pick_initial_centers(k, initial_centers, samples, test.get_kernel());
  
  Timer::Start("clustering");
  
  test.train(samples,initial_centers);
  std::vector<unsigned long> assignments = spectral_cluster(kernel_type(0.1), samples, k);
  
  Timer::Stop("clustering");
  
  // Save output, if desired.
  if (CLI::HasParam("assignments_file"))
    data::Save(CLI::GetParam<string>("assignments_file"), assignments);
  
  return 0;
}