    // The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
    /*
        This example program shows you how to create your own custom binary classification
        trainer object and use it with the multiclass classification tools in the dlib C++
        library.  This example assumes you have already become familiar with the concepts
        introduced in the multiclass_classification_ex.cpp example program.

        In this example we will create a very simple trainer object that takes a binary
        classification problem and produces a decision rule which says a test point has the
        same class as whichever centroid it is closest to.

        The multiclass training dataset will consist of four classes.  Each class will be a blob
        of points in one of the quadrants of the cartesian plane.   For fun, we will use
        std::string labels and therefore the labels of these classes will be the following:
            "upper_left",
            "upper_right",
            "lower_left",
            "lower_right"
    */

    #include <dlib/svm_threaded.h>

    #include <iostream>
    #include <vector>

    #include <dlib/rand.h>

    using namespace std;
    using namespace dlib;

    // Our data will be 2-dimensional data. So declare an appropriate type to contain these points.
    typedef matrix<double,2,1> sample_type;

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

    struct custom_decision_function
    {
        /*!
            WHAT THIS OBJECT REPRESENTS
                This object is the representation of our binary decision rule.
        !*/

        // centers of the two classes
        sample_type positive_center, negative_center;

        double operator() (
            const sample_type& x
        ) const
        {
            // if x is closer to the positive class then return +1
            if (length(positive_center - x) < length(negative_center - x))
                return +1;
            else
                return -1;
        }
    };

    // Later on in this example we will save our decision functions to disk.  This
    // pair of routines is needed for this functionality.
    void serialize (const custom_decision_function& item, std::ostream& out)
    {
        // write the state of item to the output stream
        serialize(item.positive_center, out);
        serialize(item.negative_center, out);
    }

    void deserialize (custom_decision_function& item, std::istream& in)
    {
        // read the data from the input stream and store it in item
        deserialize(item.positive_center, in);
        deserialize(item.negative_center, in);
    }

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

    class simple_custom_trainer
    {
        /*!
            WHAT THIS OBJECT REPRESENTS
                This is our example custom binary classifier trainer object.  It simply
                computes the means of the +1 and -1 classes, puts them into our
                custom_decision_function, and returns the results.

                Below we define the train() function.  I have also included the
                requires/ensures definition for a generic binary classifier's train()
        !*/
    public:

        custom_decision_function train (
            const std::vector<sample_type>& samples,
            const std::vector<double>& labels
        ) const
        /*!
            requires
                - is_binary_classification_problem(samples, labels) == true
                  (e.g. labels consists of only +1 and -1 values, samples.size() == labels.size())
            ensures
                - returns a decision function F with the following properties:
                    - if (new_x is a sample predicted have +1 label) then
                        - F(new_x) >= 0
                    - else
                        - F(new_x) < 0
        !*/
        {
            sample_type positive_center, negative_center;

            // compute sums of each class
            positive_center = 0;
            negative_center = 0;
            for (unsigned long i = 0; i < samples.size(); ++i)
            {
                if (labels[i] == +1)
                    positive_center += samples[i];
                else // this is a -1 sample
                    negative_center += samples[i];
            }

            // divide by number of +1 samples
            positive_center /= sum(mat(labels) == +1);
            // divide by number of -1 samples
            negative_center /= sum(mat(labels) == -1);

            custom_decision_function df;
            df.positive_center = positive_center;
            df.negative_center = negative_center;

            return df;
        }
    };

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

    void generate_data (
        std::vector<sample_type>& samples,
        std::vector<string>& labels
    );
    /*!
        ensures
            - make some four class data as described above.
            - each class will have 50 samples in it
    !*/

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

    int main()
    {
        std::vector<sample_type> samples;
        std::vector<string> labels;

        // First, get our labeled set of training data
        generate_data(samples, labels);

        cout << "samples.size(): "<< samples.size() << endl;

        // Define the trainer we will use.  The second template argument specifies the type
        // of label used, which is string in this case.
        typedef one_vs_one_trainer<any_trainer<sample_type>, string> ovo_trainer;

        ovo_trainer trainer;

        // Now tell the one_vs_one_trainer that, by default, it should use the simple_custom_trainer
        // to solve the individual binary classification subproblems.
        trainer.set_trainer(simple_custom_trainer());

        // Next, to make things a little more interesting, we will setup the one_vs_one_trainer
        // to use kernel ridge regression to solve the upper_left vs lower_right binary classification
        // subproblem.
        typedef radial_basis_kernel<sample_type> rbf_kernel;
        krr_trainer<rbf_kernel> rbf_trainer;
        rbf_trainer.set_kernel(rbf_kernel(0.1));
        trainer.set_trainer(rbf_trainer, "upper_left", "lower_right");

        // Now let's do 5-fold cross-validation using the one_vs_one_trainer we just setup.
        // As an aside, always shuffle the order of the samples before doing cross validation.
        // For a discussion of why this is a good idea see the svm_ex.cpp example.
        randomize_samples(samples, labels);
        cout << "cross validation: \n" << cross_validate_multiclass_trainer(trainer, samples, labels, 5) << endl;
        // This dataset is very easy and everything is correctly classified.  Therefore, the output of
        // cross validation is the following confusion matrix.
        /*
            50  0  0  0
             0 50  0  0
             0  0 50  0
             0  0  0 50
        */

        // We can also obtain the decision rule as always.
        one_vs_one_decision_function<ovo_trainer> df = trainer.train(samples, labels);

        cout << "predicted label: "<< df(samples[0])  << ", true label: "<< labels[0] << endl;
        cout << "predicted label: "<< df(samples[90]) << ", true label: "<< labels[90] << endl;
        // The output is:
        /*
            predicted label: upper_right, true label: upper_right
            predicted label: lower_left, true label: lower_left
        */

        // Finally, let's save our multiclass decision rule to disk.  Remember that we have
        // to specify the types of binary decision function used inside the one_vs_one_decision_function.
        one_vs_one_decision_function<ovo_trainer,
                custom_decision_function,                             // This is the output of the simple_custom_trainer
                decision_function<radial_basis_kernel<sample_type> >  // This is the output of the rbf_trainer
            > df2, df3;

        df2 = df;
        // save to a file called df.dat
        serialize("df.dat") << df2;

        // load the function back in from disk and store it in df3.
        deserialize("df.dat") >> df3;

        // Test df3 to see that this worked.
        cout << endl;
        cout << "predicted label: "<< df3(samples[0])  << ", true label: "<< labels[0] << endl;
        cout << "predicted label: "<< df3(samples[90]) << ", true label: "<< labels[90] << endl;
        // Test df3 on the samples and labels and print the confusion matrix.
        cout << "test deserialized function: \n" << test_multiclass_decision_function(df3, samples, labels) << endl;

    }

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

    void generate_data (
        std::vector<sample_type>& samples,
        std::vector<string>& labels
    )
    {
        const long num = 50;

        sample_type m;

        dlib::rand rnd;

        // add some points in the upper right quadrant
        m = 10, 10;
        for (long i = 0; i < num; ++i)
        {
            samples.push_back(m + randm(2,1,rnd));
            labels.push_back("upper_right");
        }

        // add some points in the upper left quadrant
        m = -10, 10;
        for (long i = 0; i < num; ++i)
        {
            samples.push_back(m + randm(2,1,rnd));
            labels.push_back("upper_left");
        }

        // add some points in the lower right quadrant
        m = 10, -10;
        for (long i = 0; i < num; ++i)
        {
            samples.push_back(m + randm(2,1,rnd));
            labels.push_back("lower_right");
        }

        // add some points in the lower left quadrant
        m = -10, -10;
        for (long i = 0; i < num; ++i)
        {
            samples.push_back(m + randm(2,1,rnd));
            labels.push_back("lower_left");
        }

    }

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