    // Copyright (C) 2010  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.

    #include "tester.h"
    #include <dlib/manifold_regularization.h>
    #include <dlib/svm.h>
    #include <dlib/rand.h>
    #include <dlib/string.h>
    #include <dlib/graph_utils_threaded.h>
    #include <vector>
    #include <sstream>
    #include <ctime>

    namespace
    {
        using namespace test;
        using namespace dlib;
        using namespace std;
        dlib::logger dlog("test.linear_manifold_regularizer");

        template <typename hash_type, typename samples_type>
        void test_find_k_nearest_neighbors_lsh(
            const samples_type& samples
        )
        {
            std::vector<sample_pair> edges1, edges2;

            find_k_nearest_neighbors(samples, cosine_distance(), 2, edges1);
            find_k_nearest_neighbors_lsh(samples, cosine_distance(), hash_type(), 2, 6, edges2, 2);

            std::sort(edges1.begin(), edges1.end(), order_by_index<sample_pair>);
            std::sort(edges2.begin(), edges2.end(), order_by_index<sample_pair>);

            DLIB_TEST_MSG(edges1.size() == edges2.size(), edges1.size() << "    " << edges2.size());
            for (unsigned long i = 0; i < edges1.size(); ++i)
            {
                DLIB_TEST(edges1[i] == edges2[i]);
                DLIB_TEST_MSG(std::abs(edges1[i].distance() - edges2[i].distance()) < 1e-7,
                    edges1[i].distance() - edges2[i].distance());
            }
        }

        template <typename scalar_type>
        void test_knn_lsh_sparse()
        {
            dlib::rand rnd;
            std::vector<std::map<unsigned long,scalar_type> > samples;
            samples.resize(20);
            for (unsigned int i = 0; i < samples.size(); ++i)
            {
                samples[i][0] = rnd.get_random_gaussian();
                samples[i][2] = rnd.get_random_gaussian();
            }

            test_find_k_nearest_neighbors_lsh<hash_similar_angles_64>(samples);
            test_find_k_nearest_neighbors_lsh<hash_similar_angles_128>(samples);
            test_find_k_nearest_neighbors_lsh<hash_similar_angles_256>(samples);
            test_find_k_nearest_neighbors_lsh<hash_similar_angles_512>(samples);
        }

        template <typename scalar_type>
        void test_knn_lsh_dense()
        {
            dlib::rand rnd;
            std::vector<matrix<scalar_type,0,1> > samples;
            samples.resize(20);
            for (unsigned int i = 0; i < samples.size(); ++i)
            {
                samples[i].set_size(2);
                samples[i](0) = rnd.get_random_gaussian();
                samples[i](1) = rnd.get_random_gaussian();
            }

            test_find_k_nearest_neighbors_lsh<hash_similar_angles_64>(samples);
            test_find_k_nearest_neighbors_lsh<hash_similar_angles_128>(samples);
            test_find_k_nearest_neighbors_lsh<hash_similar_angles_256>(samples);
            test_find_k_nearest_neighbors_lsh<hash_similar_angles_512>(samples);
        }

        class linear_manifold_regularizer_tester : public tester
        {
            /*!
                WHAT THIS OBJECT REPRESENTS
                    This object represents a unit test.  When it is constructed
                    it adds itself into the testing framework.
            !*/
        public:
            linear_manifold_regularizer_tester (
            ) :
                tester (
                    "test_linear_manifold_regularizer",       // the command line argument name for this test
                    "Run tests on the linear_manifold_regularizer object.", // the command line argument description
                    0                     // the number of command line arguments for this test
                )
            {
                seed = 1;
            }

            dlib::rand rnd;

            unsigned long seed;

            typedef matrix<double, 0, 1> sample_type;
            typedef radial_basis_kernel<sample_type> kernel_type;

            void do_the_test()
            {
                print_spinner();
                std::vector<sample_type> samples;

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

                const unsigned long num_points = 200;

                // create a large dataset with two concentric circles.
                generate_circle(samples, 1, num_points);  // circle of radius 1
                generate_circle(samples, 5, num_points);  // circle of radius 5

                std::vector<sample_pair> edges;
                find_percent_shortest_edges_randomly(samples, squared_euclidean_distance(0.1, 4), 1, 10000, "random seed", edges);

                dlog << LTRACE << "number of edges generated: " << edges.size();

                empirical_kernel_map<kernel_type> ekm;

                ekm.load(kern, randomly_subsample(samples, 100));

                // Project all the samples into the span of our 50 basis samples
                for (unsigned long i = 0; i < samples.size(); ++i)
                    samples[i] = ekm.project(samples[i]);

                // Now create the manifold regularizer.   The result is a transformation matrix that
                // embodies the manifold assumption discussed above.
                linear_manifold_regularizer<sample_type> lmr;
                lmr.build(samples, edges, use_gaussian_weights(0.1));
                matrix<double> T = lmr.get_transformation_matrix(10000);

                print_spinner();

                // generate the T matrix manually and make sure it matches.  The point of this test
                // is to make sure that the more complex version of this that happens inside the linear_manifold_regularizer
                // is correct.  It uses a tedious block of loops to do it in a way that is a lot faster for sparse
                // W matrices but isn't super straight forward.
                matrix<double> X(samples[0].size(), samples.size());
                for (unsigned long i = 0; i < samples.size(); ++i)
                    set_colm(X,i) = samples[i];

                matrix<double> W(samples.size(), samples.size());
                W = 0;
                for (unsigned long i = 0; i < edges.size(); ++i)
                {
                    W(edges[i].index1(), edges[i].index2()) = use_gaussian_weights(0.1)(edges[i]);
                    W(edges[i].index2(), edges[i].index1()) = use_gaussian_weights(0.1)(edges[i]);
                }
                matrix<double> L = diagm(sum_rows(W)) - W;
                matrix<double> trueT = inv_lower_triangular(chol(identity_matrix<double>(X.nr()) + (10000.0/sum(lowerm(W)))*X*L*trans(X)));

                dlog << LTRACE << "T error: "<< max(abs(T - trueT));
                DLIB_TEST(max(abs(T - trueT)) < 1e-7);

                print_spinner();
                // Apply the transformation generated by the linear_manifold_regularizer to
                // all our samples.
                for (unsigned long i = 0; i < samples.size(); ++i)
                    samples[i] = T*samples[i];

                // For convenience, generate a projection_function and merge the transformation
                // matrix T into it.
                projection_function<kernel_type> proj = ekm.get_projection_function();
                proj.weights = T*proj.weights;

                // Pick 2 different labeled points.  One on the inner circle and another on the outer.
                // For each of these test points we will see if using the single plane that separates
                // them is a good way to separate the concentric circles.  Also do this a bunch
                // of times with different randomly chosen points so we can see how robust the result is.
                for (int itr = 0; itr < 10; ++itr)
                {
                    print_spinner();
                    std::vector<sample_type> test_points;
                    // generate a random point from the radius 1 circle
                    generate_circle(test_points, 1, 1);
                    // generate a random point from the radius 5 circle
                    generate_circle(test_points, 5, 1);

                    // project the two test points into kernel space.  Recall that this projection_function
                    // has the manifold regularizer incorporated into it.
                    const sample_type class1_point = proj(test_points[0]);
                    const sample_type class2_point = proj(test_points[1]);

                    double num_wrong = 0;

                    // Now attempt to classify all the data samples according to which point
                    // they are closest to.  The output of this program shows that without manifold
                    // regularization this test will fail but with it it will perfectly classify
                    // all the points.
                    for (unsigned long i = 0; i < samples.size(); ++i)
                    {
                        double distance_to_class1 = length(samples[i] - class1_point);
                        double distance_to_class2 = length(samples[i] - class2_point);

                        bool predicted_as_class_1 = (distance_to_class1 < distance_to_class2);

                        bool really_is_class_1 = (i < num_points);

                        // now count how many times we make a mistake
                        if (predicted_as_class_1 != really_is_class_1)
                            ++num_wrong;
                    }

                    DLIB_TEST_MSG(num_wrong == 0, num_wrong);
                }

            }

            void generate_circle (
                std::vector<sample_type>& samples,
                double radius,
                const long num
            )
            {
                sample_type m(2,1);

                for (long i = 0; i < num; ++i)
                {
                    double sign = 1;
                    if (rnd.get_random_double() < 0.5)
                        sign = -1;
                    m(0) = 2*radius*rnd.get_random_double()-radius;
                    m(1) = sign*sqrt(radius*radius - m(0)*m(0));

                    samples.push_back(m);
                }
            }

            void test_knn1()
            {
                std::vector<matrix<double,2,1> > samples;

                matrix<double,2,1> test;

                test = 0,0;  samples.push_back(test);
                test = 1,1;  samples.push_back(test);
                test = 1,-1;  samples.push_back(test);
                test = -1,1;  samples.push_back(test);
                test = -1,-1;  samples.push_back(test);

                std::vector<sample_pair> edges;
                find_k_nearest_neighbors(samples, squared_euclidean_distance(), 1, edges);
                DLIB_TEST(edges.size() == 4);

                std::sort(edges.begin(), edges.end(), &order_by_index<sample_pair>);

                DLIB_TEST(edges[0] == sample_pair(0,1,0));
                DLIB_TEST(edges[1] == sample_pair(0,2,0));
                DLIB_TEST(edges[2] == sample_pair(0,3,0));
                DLIB_TEST(edges[3] == sample_pair(0,4,0));

                find_k_nearest_neighbors(samples, squared_euclidean_distance(), 3, edges);
                DLIB_TEST(edges.size() == 8);

                find_k_nearest_neighbors(samples, squared_euclidean_distance(3.9, 4.1), 3, edges);
                DLIB_TEST(edges.size() == 4);

                std::sort(edges.begin(), edges.end(), &order_by_index<sample_pair>);

                DLIB_TEST(edges[0] == sample_pair(1,2,0));
                DLIB_TEST(edges[1] == sample_pair(1,3,0));
                DLIB_TEST(edges[2] == sample_pair(2,4,0));
                DLIB_TEST(edges[3] == sample_pair(3,4,0));

                find_k_nearest_neighbors(samples, squared_euclidean_distance(30000, 4.1), 3, edges);
                DLIB_TEST(edges.size() == 0);
            }

            void test_knn1_approx()
            {
                std::vector<matrix<double,2,1> > samples;

                matrix<double,2,1> test;

                test = 0,0;  samples.push_back(test);
                test = 1,1;  samples.push_back(test);
                test = 1,-1;  samples.push_back(test);
                test = -1,1;  samples.push_back(test);
                test = -1,-1;  samples.push_back(test);

                std::vector<sample_pair> edges;
                find_approximate_k_nearest_neighbors(samples, squared_euclidean_distance(), 1, 10000, seed, edges);
                DLIB_TEST(edges.size() == 4);

                std::sort(edges.begin(), edges.end(), &order_by_index<sample_pair>);

                DLIB_TEST(edges[0] == sample_pair(0,1,0));
                DLIB_TEST(edges[1] == sample_pair(0,2,0));
                DLIB_TEST(edges[2] == sample_pair(0,3,0));
                DLIB_TEST(edges[3] == sample_pair(0,4,0));

                find_approximate_k_nearest_neighbors(samples, squared_euclidean_distance(), 3, 10000, seed, edges);
                DLIB_TEST(edges.size() == 8);

                find_approximate_k_nearest_neighbors(samples, squared_euclidean_distance(3.9, 4.1), 3, 10000, seed, edges);
                DLIB_TEST(edges.size() == 4);

                std::sort(edges.begin(), edges.end(), &order_by_index<sample_pair>);

                DLIB_TEST(edges[0] == sample_pair(1,2,0));
                DLIB_TEST(edges[1] == sample_pair(1,3,0));
                DLIB_TEST(edges[2] == sample_pair(2,4,0));
                DLIB_TEST(edges[3] == sample_pair(3,4,0));

                find_approximate_k_nearest_neighbors(samples, squared_euclidean_distance(30000, 4.1), 3, 10000, seed, edges);
                DLIB_TEST(edges.size() == 0);
            }

            void test_knn2()
            {
                std::vector<matrix<double,2,1> > samples;

                matrix<double,2,1> test;

                test = 1,1;  samples.push_back(test);
                test = 1,-1;  samples.push_back(test);
                test = -1,1;  samples.push_back(test);
                test = -1,-1;  samples.push_back(test);

                std::vector<sample_pair> edges;
                find_k_nearest_neighbors(samples, squared_euclidean_distance(), 2, edges);
                DLIB_TEST(edges.size() == 4);

                std::sort(edges.begin(), edges.end(), &order_by_index<sample_pair>);

                DLIB_TEST(edges[0] == sample_pair(0,1,0));
                DLIB_TEST(edges[1] == sample_pair(0,2,0));
                DLIB_TEST(edges[2] == sample_pair(1,3,0));
                DLIB_TEST(edges[3] == sample_pair(2,3,0));

                find_k_nearest_neighbors(samples, squared_euclidean_distance(), 200, edges);
                DLIB_TEST(edges.size() == 4*3/2);
            }

            void test_knn2_approx()
            {
                std::vector<matrix<double,2,1> > samples;

                matrix<double,2,1> test;

                test = 1,1;  samples.push_back(test);
                test = 1,-1;  samples.push_back(test);
                test = -1,1;  samples.push_back(test);
                test = -1,-1;  samples.push_back(test);

                std::vector<sample_pair> edges;
                // For this simple graph and high number of samples we will do we should obtain the exact
                // knn solution.
                find_approximate_k_nearest_neighbors(samples, squared_euclidean_distance(), 2, 10000, seed,  edges);
                DLIB_TEST(edges.size() == 4);

                std::sort(edges.begin(), edges.end(), &order_by_index<sample_pair>);

                DLIB_TEST(edges[0] == sample_pair(0,1,0));
                DLIB_TEST(edges[1] == sample_pair(0,2,0));
                DLIB_TEST(edges[2] == sample_pair(1,3,0));
                DLIB_TEST(edges[3] == sample_pair(2,3,0));

                find_approximate_k_nearest_neighbors(samples, squared_euclidean_distance(), 200, 10000, seed,  edges);
                DLIB_TEST(edges.size() == 4*3/2);
            }

            void perform_test (
            )
            {
                for (int i = 0; i < 5; ++i)
                {
                    do_the_test();

                    ++seed;
                    test_knn1_approx();
                    test_knn2_approx();
                }
                test_knn1();
                test_knn2();
                test_knn_lsh_sparse<double>();
                test_knn_lsh_sparse<float>();
                test_knn_lsh_dense<double>();
                test_knn_lsh_dense<float>();

            }
        };

        // Create an instance of this object.  Doing this causes this test
        // to be automatically inserted into the testing framework whenever this cpp file
        // is linked into the project.  Note that since we are inside an unnamed-namespace
        // we won't get any linker errors about the symbol a being defined multiple times.
        linear_manifold_regularizer_tester a;

    }