kmeans.cpp

#include <algorithm>
#include <assert.h>
#include <iomanip>
#include <iostream>
#include <ostream>
#include <random>
#include <sstream>
#include <vector>

#include "points.h"
#include "draw.h"

#define K 3
#define POINTS 100
#define RANGE 640


int main( int argc, char* argv[] )
{
    std::array<Point,POINTS> points;
    std::array<Centroid,K> centroids;
    const int maxIterations = 10;

    /* random initial positions
        generate K initial points and then position the rest of the 
        points around the initial K */
    assert( points.size() >= centroids.size() );
    const auto jt = std::next( points.begin(), centroids.size() );
    for( auto it=points.begin(); it!=jt; it=std::next(it) )
        it->randomise( RANGE );
    
    for( auto it=jt; it!=points.end(); it=std::next(it) )
    {
        const auto cluster = std::distance(it, points.begin()) % centroids.size();
        it->randomise( *std::next(points.begin(),cluster), RANGE );
    }

    /* random starting positions for the initial centroids */
    for( Centroid& c : centroids )
    {
        c.randomise( RANGE );
        std::cout << c << std::endl;
    }

    // do the clustering
    bool updated = true;
    for( int i=0; updated && i<maxIterations; ++i )
    {
        // k means

        // display centroid positions
        std::cout << "Iteration " << i << std::endl;
        for( auto& c : centroids )
            std::cout << c << std::endl;

        // save current state to file
        std::stringstream ss;
        ss << std::setfill('0') << std::setw(log10(maxIterations)+1) << i << "_clusters.bmp";
        
        draw( points.begin(), points.end(), 
              centroids.begin(), centroids.end(), 
              RANGE+1, RANGE+1,
              ss.str() );
    }

    return 0;
}