dlib C++ Library - chinese_whispers_abstract.h

    // Copyright (C) 2012  Davis E. King (davis@dlib.net)
    // License: Boost Software License   See LICENSE.txt for the full license.
    #undef DLIB_CHINESE_WHISPErS_ABSTRACT_Hh_
    #ifdef DLIB_CHINESE_WHISPErS_ABSTRACT_Hh_

    #include <vector>
    #include "../rand.h"
    #include "../graph_utils/ordered_sample_pair_abstract.h"
    #include "../graph_utils/sample_pair_abstract.h"

    namespace dlib
    {

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

        unsigned long chinese_whispers (
            const std::vector<ordered_sample_pair>& edges,
            std::vector<unsigned long>& labels,
            const unsigned long num_iterations,
            dlib::rand& rnd
        );
        /*!
            requires
                - is_ordered_by_index(edges) == true
            ensures
                - This function implements the graph clustering algorithm described in the
                  paper: Chinese Whispers - an Efficient Graph Clustering Algorithm and its
                  Application to Natural Language Processing Problems by Chris Biemann.
                - Interprets edges as a directed graph.  That is, it contains the edges on the
                  said graph and the ordered_sample_pair::distance() values define the edge
                  weights (larger values indicating a stronger edge connection between the
                  nodes).  If an edge has a distance() value of infinity then it is considered
                  a "must link" edge.
                - returns the number of clusters found.
                - #labels.size() == max_index_plus_one(edges)
                - for all valid i:
                    - #labels[i] == the cluster ID of the node with index i in the graph.
                    - 0 <= #labels[i] < the number of clusters found
                      (i.e. cluster IDs are assigned contiguously and start at 0)
                - Duplicate edges are interpreted as if there had been just one edge with a
                  distance value equal to the sum of all the duplicate edge's distance values.
                - The algorithm performs exactly num_iterations passes over the graph before
                  terminating.
        !*/

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

        unsigned long chinese_whispers (
            const std::vector<sample_pair>& edges,
            std::vector<unsigned long>& labels,
            const unsigned long num_iterations,
            dlib::rand& rnd
        );
        /*!
            ensures
                - This function is identical to the above chinese_whispers() routine except
                  that it operates on a vector of sample_pair objects instead of
                  ordered_sample_pairs.  Therefore, this is simply a convenience routine.  In
                  particular, it is implemented by transforming the given edges into
                  ordered_sample_pairs and then calling the chinese_whispers() routine defined
                  above.
        !*/

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

        unsigned long chinese_whispers (
            const std::vector<ordered_sample_pair>& edges,
            std::vector<unsigned long>& labels,
            const unsigned long num_iterations = 100
        );
        /*!
            requires
                - is_ordered_by_index(edges) == true
            ensures
                - performs: return chinese_whispers(edges, labels, num_iterations, rnd)
                  where rnd is a default initialized dlib::rand object.
        !*/

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

        unsigned long chinese_whispers (
            const std::vector<sample_pair>& edges,
            std::vector<unsigned long>& labels,
            const unsigned long num_iterations = 100
        );
        /*!
            ensures
                - performs: return chinese_whispers(edges, labels, num_iterations, rnd)
                  where rnd is a default initialized dlib::rand object.
        !*/

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

    }

    #endif // DLIB_CHINESE_WHISPErS_ABSTRACT_Hh_