#include <htd/main.hpp>

#include <csignal>
#include <memory>
#include <chrono>

//Create a management instance of the 'htd' library in order to allow centralized configuration.
std::unique_ptr<htd::LibraryInstance> manager(htd::createManagementInstance(htd::Id::FIRST));

/**
 *  Sample fitness function which minimizes width and height of the decomposition.
 *
 *  Width is of higher priority than height, i.e., at first, the width is minimized
 *  and if two decompositions have the same width, the one of lower height is chosen.
 */
class FitnessFunction : public htd::ITreeDecompositionFitnessFunction
{
    public:
        FitnessFunction(void)
        {

        }

        ~FitnessFunction()
        {

        }

        htd::FitnessEvaluation * fitness(const htd::IMultiHypergraph & graph,
                                         const htd::ITreeDecomposition & decomposition) const
        {
            HTD_UNUSED(graph)

            /**
              * Here we specify the fitness evaluation for a given decomposition.
              * In this case, we select the maximum bag size and the height.
              */
            return new htd::FitnessEvaluation(2,
                                              -(double)(decomposition.maximumBagSize()),
                                              -(double)(decomposition.height()));
        }

        FitnessFunction * clone(void) const
        {
            return new FitnessFunction();
        }
};

/**
 * Signal handling procedure.
 */
void handleSignal(int signal)
{
    switch (signal)
    {
        case SIGINT:
        case SIGTERM:
        {
            manager->terminate();

            break;
        }
        default:
        {
            break;
        }
    }

    std::signal(signal, handleSignal);
}

int main(int, const char * const * const)
{
    std::signal(SIGINT, handleSignal);
    std::signal(SIGTERM, handleSignal);

    std::srand(0);

    // Create a new graph instance which can handle (multi-)hyperedges.
    htd::IMutableMultiHypergraph * graph =
        manager->multiHypergraphFactory().getMultiHypergraph();

    /**
     *  Add five vertices to the sample graph.
     *  The vertices of a graph are numbered
     *  in ascending order starting from 1.
     */
    graph->addVertices(5);

    // Add two edges to the graph.
    graph->addEdge(1, 2);
    graph->addEdge(2, 3);

    // Add a hyperedge to the graph.
    graph->addEdge(std::vector<htd::vertex_t> { 5, 4, 3 });

    // Create an instance of the fitness function.
    FitnessFunction fitnessFunction;

    /**
     *  This operation changes the root of a given decomposition so that the provided
     *  fitness function is maximized. When no fitness function is provided to the
     *  constructor, the constructed optimization operation does not perform any
     *  optimization and only applies provided manipulations.
     */
    htd::TreeDecompositionOptimizationOperation * operation =
        new htd::TreeDecompositionOptimizationOperation(manager.get(), fitnessFunction);

    /**
     *  Set the previously created management instance to support graceful termination.
     */
    operation->setManagementInstance(manager.get());

    /**
     *  Set the vertex selections strategy (default = exhaustive).
     *
     *  In this case, we want to select (at most) 10 vertices of the input decomposition randomly.
     */
    operation->setVertexSelectionStrategy(new htd::RandomVertexSelectionStrategy(10));

    /**
     *  Set desired manipulations. In this case we want a nice (= normalized) tree decomposition.
     */
    operation->addManipulationOperation(new htd::NormalizationOperation(manager.get()));

    /**
      * Optionally, we can set the vertex elimination algorithm.
      * We decide to use the min-degree heuristic in this case.
      */
    manager->orderingAlgorithmFactory()
        .setConstructionTemplate(new htd::MinDegreeOrderingAlgorithm(manager.get()));

    // Get the default tree decomposition algorithm. One can also choose a custom one.
    htd::ITreeDecompositionAlgorithm * baseAlgorithm =
        manager->treeDecompositionAlgorithmFactory().getTreeDecompositionAlgorithm();

    /**
     *  Set the optimization operation as manipulation operation in order
     *  to choose the optimal root reducing height of the tree decomposition.
     */
    baseAlgorithm->addManipulationOperation(operation);

    /**
     *  Create a new instance of htd::IterativeImprovementTreeDecompositionAlgorithm based
     *  on the base algorithm and the fitness function. Note that the fitness function can
     *  be an arbiraty one and can differ from the one used in the optimization operation.
     */
    htd::IterativeImprovementTreeDecompositionAlgorithm algorithm(manager.get(),
                                                                  baseAlgorithm,
                                                                  fitnessFunction);

    /**
     *  Set the maximum number of iterations after which the best decomposition with
     *  respect to the fitness function shall be returned. Use value 1 to make the
     *  iterative algorithm return the first decomposition found.
     */
    algorithm.setIterationCount(10);

    /**
     *  Set the maximum number of iterations without improvement after which the algorithm returns
     *  best decomposition with respect to the fitness function found so far. A limit of 0 aborts
     *  the algorithm after the first non-improving solution has been found, i.e. the algorithm
     *  will perform a simple hill-climbing approach.
     */
    algorithm.setNonImprovementLimit(3);

    // Record the optimal maximal bag size of the tree decomposition to allow printing the progress.
    std::size_t optimalBagSize = (std::size_t)-1;

    /**
     *  Compute the decomposition. Note that the additional, optional parameter of the function
     *  computeDecomposition() in case of htd::IterativeImprovementTreeDecompositionAlgorithm
     *  can be used to intercept every new decomposition. In this case we output some
     *  intermediate information upon perceiving an improved decompostion.
     */
    htd::ITreeDecomposition * decomposition =
        algorithm.computeDecomposition(*graph, [&](const htd::IMultiHypergraph & graph,
                                                   const htd::ITreeDecomposition & decomposition,
                                                   const htd::FitnessEvaluation & fitness)
    {
        // Disable warnings concerning unused variables.
        HTD_UNUSED(graph)
        HTD_UNUSED(decomposition)

        std::size_t bagSize = -fitness.at(0);

        /**
         *  After each improvement we print the current optimal
         *  width + 1 and the time when the decomposition was found.
         */
        if (bagSize < optimalBagSize)
        {
            optimalBagSize = bagSize;

            std::chrono::milliseconds::rep msSinceEpoch =
                std::chrono::duration_cast<std::chrono::milliseconds>
                        (std::chrono::system_clock::now().time_since_epoch()).count();

            std::cout << "c status " << optimalBagSize << " " << msSinceEpoch << std::endl;
        }
    });

    // If a decomposition was found we want to print it to stdout.
    if (decomposition != nullptr)
    {
        /**
         *  Check whether the algorithm indeed computed a valid decomposition.
         *
         *  algorithm.isSafelyInterruptible() for decomposition algorithms allows to
         *  find out if the algorithm is safely interruptible. If the getter returns
         *  true, the algorithm guarantees that the computed decomposition is indeed
         *  a valid one and that all manipulations and all labelings were applied
         *  successfully.
         */
        if (!manager->isTerminated() || algorithm.isSafelyInterruptible())
        {
            // Print the height of the decomposition to stdout.
            std::cout << decomposition->height() << std::endl;
            
            // Print the size of the largest bag of the decomposition to stdout.
            std::cout << decomposition->maximumBagSize() << std::endl;
        }

        delete decomposition;
    }

    delete graph;

    return 0;
}