```// Copyright (C) 2012  Davis E. King (davis@dlib.net)
#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
- 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_

```