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

    #include "simplify_linear_decision_function_abstract.h"
    #include "../algs.h"
    #include "function.h"
    #include "sparse_kernel.h"
    #include "kernel.h"
    #include <map>
    #include <vector>

    namespace dlib
    {

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

        template <
            typename T
            >
        decision_function<sparse_linear_kernel<T> > simplify_linear_decision_function (
            const decision_function<sparse_linear_kernel<T> >& df
        )
        {
            // don't do anything if we don't have to
            if (df.basis_vectors.size() <= 1)
                return df;

            decision_function<sparse_linear_kernel<T> > new_df;

            new_df.b = df.b;
            new_df.basis_vectors.set_size(1);
            new_df.alpha.set_size(1);
            new_df.alpha(0) = 1;

            // now compute the weighted sum of all the sparse basis_vectors in df
            typedef typename T::value_type pair_type;
            typedef typename pair_type::first_type key_type;
            typedef typename pair_type::second_type value_type;
            std::map<key_type, value_type> accum;
            for (long i = 0; i < df.basis_vectors.size(); ++i)
            {
                typename T::const_iterator j = df.basis_vectors(i).begin();
                const typename T::const_iterator end = df.basis_vectors(i).end();
                for (; j != end; ++j)
                {
                    accum[j->first] += df.alpha(i) * (j->second);
                }
            }

            new_df.basis_vectors(0) = T(accum.begin(), accum.end());

            return new_df;
        }

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

        template <
            typename T
            >
        decision_function<linear_kernel<T> > simplify_linear_decision_function (
            const decision_function<linear_kernel<T> >& df
        )
        {
            // don't do anything if we don't have to
            if (df.basis_vectors.size() <= 1)
                return df;

            decision_function<linear_kernel<T> > new_df;

            new_df.b = df.b;
            new_df.basis_vectors.set_size(1);
            new_df.alpha.set_size(1);
            new_df.alpha(0) = 1;

            // now compute the weighted sum of all the basis_vectors in df
            new_df.basis_vectors(0) = 0;
            for (long i = 0; i < df.basis_vectors.size(); ++i)
            {
                new_df.basis_vectors(0) += df.alpha(i) * df.basis_vectors(i);
            }

            return new_df;
        }

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

        template <
            typename T
            >
        decision_function<linear_kernel<T> > simplify_linear_decision_function (
            const normalized_function<decision_function<linear_kernel<T> >, vector_normalizer<T> >& df
        )
        {
            decision_function<linear_kernel<T> > new_df = simplify_linear_decision_function(df.function);

            // now incorporate the normalization stuff into new_df
            new_df.basis_vectors(0) = pointwise_multiply(new_df.basis_vectors(0), df.normalizer.std_devs());
            new_df.b += dot(new_df.basis_vectors(0), df.normalizer.means());

            return new_df;
        }

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

    }

    #endif // DLIB_SIMPLIFY_LINEAR_DECiSION_FUNCTION_Hh_