// Copyright (C) 2008  Davis E. King (davisking@users.sourceforge.net)
// License: Boost Software License   See LICENSE.txt for the full license.
#undef DLIB_KKMEANs_ABSTRACT_
#ifdef DLIB_KKMEANs_ABSTRACT_

#include <cmath>
#include "../matrix/matrix_abstract.h"
#include "../algs.h"
#include "../serialize.h"
#include "kernel_abstract.h"
#include "kcentroid_abstract.h"
#include "../noncopyable.h"

namespace dlib
{

    template <
        typename kernel_type 
        >
    class kkmeans : public noncopyable
    {
        /*!
            REQUIREMENTS ON kernel_type
                is a kernel function object as defined in dlib/svm/kernel_abstract.h 

            INITIAL VALUE
                - number_of_centers() == 1

            WHAT THIS OBJECT REPRESENTS
                This is an implementation of a kernelized k-means clustering algorithm.  
                It performs k-means clustering by using the kcentroid object.  
        !*/

    public:
        typedef typename kernel_type::scalar_type scalar_type;
        typedef typename kernel_type::sample_type sample_type;
        typedef typename kernel_type::mem_manager_type mem_manager_type;

        kkmeans (
            const kcentroid<kernel_type>& kc_ 
        );
        /*!
            ensures
                - #number_of_centers() == 1
                - #get_kcentroid(0) == a copy of kc_
        !*/

        ~kkmeans(
        );
        /*!
            ensures
                - all resources associated with *this have been released
        !*/

        void set_kcentroid (
            const kcentroid<kernel_type>& kc_
        );
        /*!
            ensures
                - for all idx:  
                    - #get_kcentroid(idx) == a copy of kc_
        !*/

        const kcentroid<kernel_type>& get_kcentroid (
            unsigned long i
        ) const;
        /*!
            ensures
                - returns a const reference to the ith kcentroid object contained in
                  this object.  Each kcentroid represents one of the centers found
                  by the k-means clustering algorithm.
        !*/

        const kernel_type& get_kernel (
        ) const;
        /*!
            ensures
                - returns a const reference to the kernel used by this object
        !*/

        void set_number_of_centers (
            unsigned long num
        );
        /*!
            requires
                - num > 0
            ensures
                - #number_of_centers() == num
        !*/

        unsigned long number_of_centers (
        ) const;
        /*!
            ensures
                - returns the number of centers used in this instance of the k-means clustering
                  algorithm.
        !*/

        template <
            typename matrix_type,
            typename matrix_type2
            >
        void train (
            const matrix_type& samples,
            const matrix_type2& initial_centers,
            long max_iter = 1000000
        );
        /*!
            requires
                - matrix_type::type == sample_type  (i.e. matrix_type should contain sample_type objects)
                - matrix_type2::type == sample_type (i.e. matrix_type2 should contain sample_type objects)
                - initial_centers.nc() == 1         (i.e. must be a column vector)
                - samples.nc() == 1                 (i.e. must be a column vector)
                - initial_centers.nr() == number_of_centers()
            ensures
                - performs k-means clustering of the given set of samples.  The initial center points
                  are taken from the initial_centers argument.
                - loops over the data and continues to refine the clustering until either the cluster centers
                  don't move or we have done max_iter iterations over the data.
                - After this function finishes you can call the operator() function below
                  to determine which centroid a given sample is closest to.
        !*/

        unsigned long operator() (
            const sample_type& sample
        ) const;
        /*!
            ensures
                - returns a number idx such that:
                    - idx < number_of_centers()
                    - get_kcentroid(idx) == the centroid that is closest to the given
                      sample.
        !*/

        void swap (
            kkmeans& item
        );
        /*!
            ensures
                - swaps *this and item
        !*/
    };

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

    template <
        typename kernel_type
        >
    void swap(
        kkmeans<kernel_type>& a, 
        kkmeans<kernel_type>& b
    ) { a.swap(b); }
    /*!
        provides a global swap function
    !*/

    template <
        typename kernel_type
        >
    void serialize (
        const kkmeans<kernel_type>& item,
        std::ostream& out
    );
    /*!
        provides serialization support for kkmeans objects
    !*/

    template <
        typename kernel_type 
        >
    void deserialize (
        kkmeans<kernel_type>& item,
        std::istream& in 
    );
    /*!
        provides serialization support for kkmeans objects
    !*/

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

    template <
        typename vector_type, 
        typename kernel_type
        >
    void pick_initial_centers(
        long num_centers, 
        vector_type& centers, 
        const vector_type& samples, 
        const kernel_type& k, 
        double percentile = 0.01
    );
    /*!
        requires
            - num_centers > 1
            - 0 <= percentile < 1
            - samples.size() > 1
            - vector_type == something with an interface compatible with std::vector
            - k(samples[0],samples[0]) must be a valid expression that returns a double
        ensures
            - finds num_centers candidate cluster centers in the data in the samples 
              vector.  Assumes that k is the kernel that will be used during clustering 
              to define the space in which clustering occurs.
            - The centers are found by looking for points that are far away from other 
              candidate centers.  However, if the data is noisy you probably want to 
              ignore the farthest way points since they will be outliers.  To do this 
              set percentile to the fraction of outliers you expect the data to contain.
            - #centers.size() == num_centers
            - #centers == a vector containing the candidate centers found
    !*/

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

}

#endif // DLIB_KKMEANs_ABSTRACT_

