    // Copyright (C) 2014  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #ifndef DLIB_LDA_Hh_
    #define DLIB_LDA_Hh_

    #include "lda_abstract.h"
    #include "../algs.h"
    #include <map>
    #include "../matrix.h"
    #include <vector>

    namespace dlib
    {

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

        namespace impl
        {

            inline std::map<unsigned long,unsigned long> make_class_labels(
                const std::vector<unsigned long>& row_labels
            )
            {
                std::map<unsigned long,unsigned long> class_labels;
                for (size_t i = 0; i < row_labels.size(); ++i)
                {
                    const unsigned long next = class_labels.size();
                    if (class_labels.count(row_labels[i]) == 0)
                        class_labels[row_labels[i]] = next;
                }
                return class_labels;
            }

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

            template <
                typename T
                >
            matrix<T,0,1> center_matrix (
                matrix<T>& X
            )
            {
                matrix<T,1> mean;
                for (long r = 0; r < X.nr(); ++r)
                    mean += rowm(X,r);
                mean /= X.nr();

                for (long r = 0; r < X.nr(); ++r)
                    set_rowm(X,r) -= mean;

                return trans(mean);
            }
        }

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

        template <
            typename T
            >
        void compute_lda_transform (
            matrix<T>& X,
            matrix<T,0,1>& mean,
            const std::vector<unsigned long>& row_labels,
            unsigned long lda_dims = 500,
            unsigned long extra_pca_dims = 200
        )
        {
            std::map<unsigned long,unsigned long> class_labels = impl::make_class_labels(row_labels);
            // LDA can only give out at most class_labels.size()-1 dimensions so don't try to
            // compute more than that.
            lda_dims = std::min<unsigned long>(lda_dims, class_labels.size()-1);

            // make sure requires clause is not broken
            DLIB_CASSERT(class_labels.size() > 1,
                "\t void compute_lda_transform()"
                << "\n\t You can't call this function if the number of distinct class labels is less than 2."
                );
            DLIB_CASSERT(X.size() != 0 && (long)row_labels.size() == X.nr() && lda_dims != 0,
                "\t void compute_lda_transform()"
                << "\n\t Invalid inputs were given to this function."
                << "\n\t X.size():          " << X.size()
                << "\n\t row_labels.size(): " << row_labels.size()
                << "\n\t lda_dims:          " << lda_dims
                );

            mean = impl::center_matrix(X);
            // Do PCA to reduce dims
            matrix<T> pu,pw,pv;
            svd_fast(X, pu, pw, pv, lda_dims+extra_pca_dims, 4);
            pu.set_size(0,0); // free RAM, we don't need pu.
            X = X*pv;

            matrix<T> class_means(class_labels.size(), X.nc());
            class_means = 0;
            matrix<T,0,1> class_counts(class_labels.size());
            class_counts = 0;

            // First compute the means of each class
            for (unsigned long i = 0; i < row_labels.size(); ++i)
            {
                const unsigned long class_idx = class_labels[row_labels[i]];
                set_rowm(class_means,class_idx) += rowm(X,i);
                class_counts(class_idx)++;
            }
            class_means = inv(diagm(class_counts))*class_means;
            // subtract means from the data
            for (unsigned long i = 0; i < row_labels.size(); ++i)
            {
                const unsigned long class_idx = class_labels[row_labels[i]];
                set_rowm(X,i) -= rowm(class_means,class_idx);
            }

            // Note that we are using the formulas from the paper Using Discriminant
            // Eigenfeatures for Image Retrieval by Swets and Weng.
            matrix<T> Sw = trans(X)*X;
            matrix<T> Sb = trans(class_means)*class_means;
            matrix<T> A, H;
            matrix<T,0,1> W;
            svd3(Sw, A, W, H);
            W = sqrt(W);
            W = reciprocal(lowerbound(W,max(W)*1e-5));
            A = trans(H*diagm(W))*Sb*H*diagm(W);
            matrix<T> v,s,u;
            svd3(A, v, s, u);
            matrix<T> tform = H*diagm(W)*u;
            // pick out only the number of dimensions we are supposed to for the output, unless
            // we should just keep them all, then don't do anything.
            if ((long)lda_dims <= tform.nc())
            {
                rsort_columns(tform, s);
                tform = colm(tform, range(0, lda_dims-1));
            }

            X = trans(pv*tform);
            mean = X*mean;
        }

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

        struct roc_point
        {
            double true_positive_rate;
            double false_positive_rate;
            double detection_threshold;
        };

        inline std::vector<roc_point> compute_roc_curve (
            const std::vector<double>& true_detections,
            const std::vector<double>& false_detections
        )
        {
            DLIB_CASSERT(true_detections.size() != 0);
            DLIB_CASSERT(false_detections.size() != 0);

            std::vector<std::pair<double,int> > temp;
            temp.reserve(true_detections.size()+false_detections.size());
            // We use -1 for true labels and +1 for false so when we call std::sort() below it will sort
            // runs with equal detection scores so false come first.  This will avoid it seeming like we
            // can separate true from false when scores are equal in the loop below.
            const int true_label = -1;
            const int false_label = +1;
            for (unsigned long i = 0; i < true_detections.size(); ++i)
                temp.push_back(std::make_pair(true_detections[i], true_label));
            for (unsigned long i = 0; i < false_detections.size(); ++i)
                temp.push_back(std::make_pair(false_detections[i], false_label));

            std::sort(temp.rbegin(), temp.rend());

            std::vector<roc_point> roc_curve;
            roc_curve.reserve(temp.size());

            double num_false_included = 0;
            double num_true_included = 0;
            for (unsigned long i = 0; i < temp.size(); ++i)
            {
                if (temp[i].second == true_label)
                    num_true_included++;
                else
                    num_false_included++;

                roc_point p;
                p.true_positive_rate = num_true_included/true_detections.size();
                p.false_positive_rate = num_false_included/false_detections.size();
                p.detection_threshold = temp[i].first;
                roc_curve.push_back(p);
            }

            return roc_curve;
        }

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

        inline std::pair<double,double> equal_error_rate (
            const std::vector<double>& low_vals,
            const std::vector<double>& high_vals
        )
        {
            if (low_vals.size() == 0 && high_vals.size() == 0)
                return std::make_pair(0,0);
            else if (low_vals.size() == 0)
                return std::make_pair(0, min(mat(high_vals)));
            else if (high_vals.size() == 0)
                return std::make_pair(0, max(mat(low_vals))+1);

            // Find the point of equal error rates
            double best_thresh = 0;
            double best_error = 0;
            double best_delta = std::numeric_limits<double>::infinity();
            for (const auto& pt : compute_roc_curve(high_vals, low_vals))
            {
                const double false_negative_rate = 1-pt.true_positive_rate;
                const double delta = std::abs(false_negative_rate - pt.false_positive_rate);
                if (delta < best_delta)
                {
                    best_delta = delta;
                    best_error = std::max(false_negative_rate, pt.false_positive_rate);
                    best_thresh = pt.detection_threshold;
                }
            }

            return std::make_pair(best_error, best_thresh);
        }

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

    }

    #endif // DLIB_LDA_Hh_