// Copyright (C) 2011  Davis E. King (davis@dlib.net)
// License: Boost Software License   See LICENSE.txt for the full license.


#include <dlib/matrix.h>
#include <sstream>
#include <string>
#include <cstdlib>
#include <ctime>
#include <vector>
#include "../stl_checked.h"
#include "../array.h"
#include "../rand.h"
#include "checkerboard.h"
#include <dlib/statistics.h>

#include "tester.h"
#include <dlib/svm_threaded.h>


namespace  
{

    using namespace test;
    using namespace dlib;
    using namespace std;

    logger dlog("test.probabilistic");

// ----------------------------------------------------------------------------------------

    class test_probabilistic : public tester
    {
    public:
        test_probabilistic (
        ) :
            tester ("test_probabilistic",
                    "Runs tests on the probabilistic trainer adapter.")
        {}

        void perform_test (
        )
        {
            print_spinner();


            typedef double scalar_type;
            typedef matrix<scalar_type,2,1> sample_type;

            std::vector<sample_type> x;
            std::vector<matrix<double,0,1> > x_linearized;
            std::vector<scalar_type> y;

            get_checkerboard_problem(x,y, 1000, 2);

            random_subset_selector<sample_type> rx;
            random_subset_selector<scalar_type> ry;
            rx.set_max_size(x.size());
            ry.set_max_size(x.size());

            dlog << LINFO << "pos labels: "<< sum(mat(y) == +1);
            dlog << LINFO << "neg labels: "<< sum(mat(y) == -1);

            for (unsigned long i = 0; i < x.size(); ++i)
            {
                rx.add(x[i]);
                ry.add(y[i]);
            }

            const scalar_type gamma = 2.0;

            typedef radial_basis_kernel<sample_type> kernel_type;

            krr_trainer<kernel_type> krr_trainer;
            krr_trainer.use_classification_loss_for_loo_cv();
            krr_trainer.set_kernel(kernel_type(gamma));
            krr_trainer.set_basis(randomly_subsample(x, 100));
            probabilistic_decision_function<kernel_type> df;

            dlog << LINFO << "cross validation: " << cross_validate_trainer(krr_trainer, rx,ry, 4);
            print_spinner();

            running_stats<scalar_type> rs_pos, rs_neg;

            print_spinner();
            df = probabilistic(krr_trainer,3).train(x, y);
            for (unsigned long i = 0; i < x.size(); ++i)
            {
                if (y[i] > 0)
                    rs_pos.add(df(x[i]));
                else
                    rs_neg.add(df(x[i]));
            }
            dlog << LINFO << "rs_pos.mean(): "<< rs_pos.mean();
            dlog << LINFO << "rs_neg.mean(): "<< rs_neg.mean();
            DLIB_TEST_MSG(rs_pos.mean() > 0.95, rs_pos.mean());
            DLIB_TEST_MSG(rs_neg.mean() < 0.05, rs_neg.mean());
            rs_pos.clear();
            rs_neg.clear();


            print_spinner();
            df = probabilistic(krr_trainer,3).train(rx, ry);
            for (unsigned long i = 0; i < x.size(); ++i)
            {
                if (y[i] > 0)
                    rs_pos.add(df(x[i]));
                else
                    rs_neg.add(df(x[i]));
            }
            dlog << LINFO << "rs_pos.mean(): "<< rs_pos.mean();
            dlog << LINFO << "rs_neg.mean(): "<< rs_neg.mean();
            DLIB_TEST_MSG(rs_pos.mean() > 0.95, rs_pos.mean());
            DLIB_TEST_MSG(rs_neg.mean() < 0.05, rs_neg.mean());
            rs_pos.clear();
            rs_neg.clear();

            // There can be as many folds as there are samples of the smaller class.
            print_spinner();
            std::vector<sample_type> few_x;
            std::vector<scalar_type> few_y;
            long num_pos = 0, num_neg = 0;
            for (unsigned long i = 0; i < x.size(); ++i)
            {
                if ((y[i] > 0 && num_pos++ < 3) || (y[i] < 0 && num_neg++ < 47))
                {
                    few_x.push_back(x[i]);
                    few_y.push_back(y[i]);
                }
            }
            DLIB_TEST(sum(mat(few_y) > 0) == 3);
            DLIB_TEST(sum(mat(few_y) < 0) == 47);
            for (int sign : {1, -1})
            {
                std::vector<scalar_type> labels = few_y;
                for (auto& label : labels)
                    label *= sign;
                df = train_probabilistic_decision_function(krr_trainer, few_x, labels, 3);
                DLIB_TEST(std::isfinite(df.alpha) && std::isfinite(df.beta));
                const auto adapted = probabilistic(krr_trainer, 3).train(few_x, labels);
                for (const auto& sample : few_x)
                {
                    const auto probability = df(sample);
                    DLIB_TEST(probability >= 0 && probability <= 1);
                    DLIB_TEST(std::abs(probability - adapted(sample)) < 1e-12);
                }
            }

        }
    } a;

}