    // Copyright (C) 2010  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.h>

    namespace
    {

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

        logger dlog("test.svm_c_linear");

        typedef matrix<double, 0, 1> sample_type;
        typedef std::vector<std::pair<unsigned int, double> > sparse_sample_type;

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

        void run_prior_test()
        {
            typedef matrix<double,3,1> sample_type;
            typedef linear_kernel<sample_type> kernel_type;

            svm_c_linear_trainer<kernel_type> trainer;

            std::vector<sample_type> samples;
            std::vector<double> labels;

            sample_type samp;
            samp = 0, 0, 1; samples.push_back(samp); labels.push_back(+1);
            samp = 0, 1, 0; samples.push_back(samp); labels.push_back(-1);

            trainer.set_c(10);
            decision_function<kernel_type> df = trainer.train(samples, labels);

            trainer.set_prior(df);

            samples.clear();
            labels.clear();
            samp = 1, 0, 0; samples.push_back(samp); labels.push_back(+1);
            samp = 0, 1, 0; samples.push_back(samp); labels.push_back(-1);

            df = trainer.train(samples, labels);

            samp = 0, 0, 1; samples.push_back(samp); labels.push_back(+1);
            matrix<double,1,2> rs = test_binary_decision_function(df, samples, labels);
            dlog << LINFO << rs;
            DLIB_TEST(rs(0) == 1);
            DLIB_TEST(rs(1) == 1);

            dlog << LINFO << trans(df.basis_vectors(0));
            DLIB_TEST(df.basis_vectors(0)(0) > 0);
            DLIB_TEST(df.basis_vectors(0)(1) < 0);
            DLIB_TEST(df.basis_vectors(0)(2) > 0);
        }

        void run_prior_sparse_test()
        {
            typedef std::map<unsigned long,double> sample_type;
            typedef sparse_linear_kernel<sample_type> kernel_type;

            svm_c_linear_trainer<kernel_type> trainer;

            std::vector<sample_type> samples;
            std::vector<double> labels;

            sample_type samp;
            samp[0] = 1; samples.push_back(samp); labels.push_back(+1); samp.clear();
            samp[1] = 1; samples.push_back(samp); labels.push_back(-1); samp.clear();

            trainer.set_c(10);
            decision_function<kernel_type> df = trainer.train(samples, labels);

            trainer.set_prior(df);

            samples.clear();
            labels.clear();
            samp[2] = 1; samples.push_back(samp); labels.push_back(+1); samp.clear();
            samp[1] = 1; samples.push_back(samp); labels.push_back(-1); samp.clear();

            df = trainer.train(samples, labels);

            matrix<double,1,2> rs = test_binary_decision_function(df, samples, labels);
            dlog << LINFO << rs;
            DLIB_TEST(rs(0) == 1);
            DLIB_TEST(rs(1) == 1);

            matrix<double,0,1> w = sparse_to_dense(df.basis_vectors(0));
            dlog << LINFO << trans(w);
            DLIB_TEST(w(0) > 0.1);
            DLIB_TEST(w(1) < -0.1);
            DLIB_TEST(w(2) > 0.1);
        }

        void get_simple_points (
            std::vector<sample_type>& samples,
            std::vector<double>& labels
        )
        {
            samples.clear();
            labels.clear();
            sample_type samp(2);

            samp = 0,0;
            samples.push_back(samp);
            labels.push_back(-1);

            samp = 0,1;
            samples.push_back(samp);
            labels.push_back(-1);

            samp = 3,0;
            samples.push_back(samp);
            labels.push_back(+1);

            samp = 3,1;
            samples.push_back(samp);
            labels.push_back(+1);
        }

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

        void get_simple_points_sparse (
            std::vector<sparse_sample_type>& samples,
            std::vector<double>& labels
        )
        {
            samples.clear();
            labels.clear();
            sparse_sample_type samp;

            samp.push_back(make_pair(0, 0.0));
            samp.push_back(make_pair(1, 0.0));
            samples.push_back(samp);
            labels.push_back(-1);

            samp.clear();
            samp.push_back(make_pair(0, 0.0));
            samp.push_back(make_pair(1, 1.0));
            samples.push_back(samp);
            labels.push_back(-1);

            samp.clear();
            samp.push_back(make_pair(0, 3.0));
            samp.push_back(make_pair(1, 0.0));
            samples.push_back(samp);
            labels.push_back(+1);

            samp.clear();
            samp.push_back(make_pair(0, 3.0));
            samp.push_back(make_pair(1, 1.0));
            samples.push_back(samp);
            labels.push_back(+1);
        }

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

        void test_sparse (
        )
        {
            print_spinner();
            dlog << LINFO << "test with sparse vectors";
            std::vector<sparse_sample_type> samples;
            std::vector<double> labels;

            sample_type samp;

            get_simple_points_sparse(samples,labels);

            svm_c_linear_trainer<sparse_linear_kernel<sparse_sample_type> > trainer;
            trainer.set_c(1e4);
            //trainer.be_verbose();
            trainer.set_epsilon(1e-11);

            double obj;
            decision_function<sparse_linear_kernel<sparse_sample_type> > df = trainer.train(samples, labels, obj);
            dlog << LDEBUG << "obj: "<< obj;
            DLIB_TEST_MSG(abs(obj - 0.72222222222) < 1e-7, obj);

            DLIB_TEST(abs(df(samples[0]) - (-1)) < 1e-6);
            DLIB_TEST(abs(df(samples[1]) - (-1)) < 1e-6);
            DLIB_TEST(abs(df(samples[2]) - (1)) < 1e-6);
            DLIB_TEST(abs(df(samples[3]) - (1)) < 1e-6);

            // While we are at it, make sure the krr_trainer works with sparse samples
            krr_trainer<sparse_linear_kernel<sparse_sample_type> > krr;

            df = krr.train(samples, labels);
            DLIB_TEST(abs(df(samples[0]) - (-1)) < 1e-6);
            DLIB_TEST(abs(df(samples[1]) - (-1)) < 1e-6);
            DLIB_TEST(abs(df(samples[2]) - (1)) < 1e-6);
            DLIB_TEST(abs(df(samples[3]) - (1)) < 1e-6);

            // Now test some of the sparse helper functions
            DLIB_TEST(max_index_plus_one(samples) == 2);
            DLIB_TEST(max_index_plus_one(samples[0]) == 2);

            matrix<double,3,1> m;
            m = 1;
            add_to(m, samples[3]);
            DLIB_TEST(m(0) == 1 + samples[3][0].second);
            DLIB_TEST(m(1) == 1 + samples[3][1].second);
            DLIB_TEST(m(2) == 1);

            m = 1;
            subtract_from(m, samples[3]);
            DLIB_TEST(m(0) == 1 - samples[3][0].second);
            DLIB_TEST(m(1) == 1 - samples[3][1].second);
            DLIB_TEST(m(2) == 1);

            m = 1;
            add_to(m, samples[3], 2);
            DLIB_TEST(m(0) == 1 + 2*samples[3][0].second);
            DLIB_TEST(m(1) == 1 + 2*samples[3][1].second);
            DLIB_TEST(m(2) == 1);

            m = 1;
            subtract_from(m, samples[3], 2);
            DLIB_TEST(m(0) == 1 - 2*samples[3][0].second);
            DLIB_TEST(m(1) == 1 - 2*samples[3][1].second);
            DLIB_TEST(m(2) == 1);

        }

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

        void test_dense (
        )
        {
            print_spinner();
            dlog << LINFO << "test with dense vectors";
            std::vector<sample_type> samples;
            std::vector<double> labels;

            sample_type samp;

            get_simple_points(samples,labels);

            svm_c_linear_trainer<linear_kernel<sample_type> > trainer;
            trainer.set_c(1e4);
            //trainer.be_verbose();
            trainer.set_epsilon(1e-11);

            double obj;
            decision_function<linear_kernel<sample_type> > df = trainer.train(samples, labels, obj);
            dlog << LDEBUG << "obj: "<< obj;
            DLIB_TEST_MSG(abs(obj - 0.72222222222) < 1e-7, abs(obj - 0.72222222222));
            // There shouldn't be any margin violations since this dataset is so trivial.  So that means the objective
            // should be exactly the squared norm of the decision plane (times 0.5).
            DLIB_TEST_MSG(abs(length_squared(df.basis_vectors(0))*0.5 + df.b*df.b*0.5 - 0.72222222222) < 1e-7,
                          length_squared(df.basis_vectors(0))*0.5 + df.b*df.b*0.5);

            DLIB_TEST(abs(df(samples[0]) - (-1)) < 1e-6);
            DLIB_TEST(abs(df(samples[1]) - (-1)) < 1e-6);
            DLIB_TEST(abs(df(samples[2]) - (1)) < 1e-6);
            DLIB_TEST(abs(df(samples[3]) - (1)) < 1e-6);
        }

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

        class tester_svm_c_linear : public tester
        {
        public:
            tester_svm_c_linear (
            ) :
                tester ("test_svm_c_linear",
                        "Runs tests on the svm_c_linear_trainer.")
            {}

            void perform_test (
            )
            {
                test_dense();
                test_sparse();
                run_prior_test();
                run_prior_sparse_test();

                // test mixed sparse and dense dot products
                {
                    std::map<unsigned int, double> sv;
                    matrix<double,0,1> dv(4);

                    dv = 1,2,3,4;

                    sv[0] = 1;
                    sv[3] = 1;

                    DLIB_TEST(dot(sv,dv) == 5);
                    DLIB_TEST(dot(dv,sv) == 5);
                    DLIB_TEST(dot(dv,dv) == 30);
                    DLIB_TEST(dot(sv,sv) == 2);

                    sv[10] = 9;
                    DLIB_TEST(dot(sv,dv) == 5);
                }

                // test mixed sparse dense assignments
                {
                    std::map<unsigned int, double> sv, sv2;
                    std::vector<std::pair<unsigned int, double> > sv3;
                    matrix<double,0,1> dv(4), dv2;

                    dv = 1,2,3,4;

                    sv[0] = 1;
                    sv[3] = 1;

                    assign(dv2, dv);

                    DLIB_TEST(dv2.size() == 4);
                    DLIB_TEST(dv2(0) == 1);
                    DLIB_TEST(dv2(1) == 2);
                    DLIB_TEST(dv2(2) == 3);
                    DLIB_TEST(dv2(3) == 4);

                    assign(sv2, dv);
                    DLIB_TEST(sv2.size() == 4);
                    DLIB_TEST(sv2[0] == 1);
                    DLIB_TEST(sv2[1] == 2);
                    DLIB_TEST(sv2[2] == 3);
                    DLIB_TEST(sv2[3] == 4);

                    assign(sv2, sv);
                    DLIB_TEST(sv2.size() == 2);
                    DLIB_TEST(sv2[0] == 1);
                    DLIB_TEST(sv2[1] == 0);
                    DLIB_TEST(sv2[2] == 0);
                    DLIB_TEST(sv2[3] == 1);

                    assign(sv3, sv);
                    DLIB_TEST(sv3.size() == 2);
                    DLIB_TEST(sv3[0].second == 1);
                    DLIB_TEST(sv3[1].second == 1);
                    DLIB_TEST(sv3[0].first == 0);
                    DLIB_TEST(sv3[1].first == 3);

                    assign(sv3, dv);
                    DLIB_TEST(sv3.size() == 4);
                    DLIB_TEST(sv3[0].second == 1);
                    DLIB_TEST(sv3[1].second == 2);
                    DLIB_TEST(sv3[2].second == 3);
                    DLIB_TEST(sv3[3].second == 4);
                    DLIB_TEST(sv3[0].first == 0);
                    DLIB_TEST(sv3[1].first == 1);
                    DLIB_TEST(sv3[2].first == 2);
                    DLIB_TEST(sv3[3].first == 3);

                    assign(sv3, sv);
                    DLIB_TEST(sv3.size() == 2);
                    DLIB_TEST(sv3[0].second == 1);
                    DLIB_TEST(sv3[1].second == 1);
                    DLIB_TEST(sv3[0].first == 0);
                    DLIB_TEST(sv3[1].first == 3);

                    sv.clear();
                    assign(sv, sv3);
                    DLIB_TEST(sv.size() == 2);
                    DLIB_TEST(sv[0] == 1);
                    DLIB_TEST(sv[1] == 0);
                    DLIB_TEST(sv[2] == 0);
                    DLIB_TEST(sv[3] == 1);

                }
            }
        } a;

    }