examples/tutorial/k-means_step4.cpp

This example is part of the k-means tutorial. See Step 4: Iteration!

/*******************************************************************************
 * examples/tutorial/k-means_step4.cpp
 *
 * Part of Project Thrill - http://project-thrill.org
 *
 * Copyright (C) 2016 Timo Bingmann <[email protected]>
 *
 * All rights reserved. Published under the BSD-2 license in the LICENSE file.
 ******************************************************************************/

//! \example examples/tutorial/k-means_step4.cpp
//!
//! This example is part of the k-means tutorial. See \ref kmeans_tutorial_step4

#include <thrill/api/all_gather.hpp>
#include <thrill/api/cache.hpp>
#include <thrill/api/generate.hpp>
#include <thrill/api/print.hpp>
#include <thrill/api/reduce_by_key.hpp>
#include <thrill/api/sample.hpp>

#include <ostream>
#include <random>
#include <vector>

//! A 2-dimensional point with double precision
struct Point {
    //! point coordinates
    double x, y;

    double DistanceSquare(const Point& b) const {
        return (x - b.x) * (x - b.x) + (y - b.y) * (y - b.y);
    }
    Point operator + (const Point& b) const {
        return Point { x + b.x, y + b.y };
    }
    Point operator / (double s) const {
        return Point { x / s, y / s };
    }
};

//! make ostream-able for Print()
std::ostream& operator << (std::ostream& os, const Point& p) {
    return os << '(' << p.x << ',' << p.y << ')';
}

//! Assignment of a point to a cluster.
struct ClosestCenter {
    size_t cluster_id;
    Point  point;
    size_t count;
};
//! make ostream-able for Print()
std::ostream& operator << (std::ostream& os, const ClosestCenter& cc) {
    return os << '(' << cc.cluster_id
              << ':' << cc.point << ':' << cc.count << ')';
}

//! our main processing method
auto Process(thrill::Context& ctx) {

    std::default_random_engine rng(std::random_device { } ());
    std::uniform_real_distribution<double> dist(0.0, 1000.0);

    // generate 100 random points using uniform distribution
    auto points =
        Generate(
            ctx, /* size */ 100,
            [&](const size_t&) {
                return Point { dist(rng), dist(rng) };
            })
        .Cache();

    // print out the points
    points.Print("points");

    //! [step4 iteration loop]
    // pick some initial random cluster centers
    thrill::DIA<Point> centers = points.Sample(/* num_clusters */ 10);

    for (size_t iter = 0; iter < /* iterations */ 10; ++iter)
    {
        // collect centers in a local vector on each worker
        std::vector<Point> local_centers = centers.AllGather();

        auto new_centers =
            points
            // calculate the closest center for each point
            .Map(
                [local_centers](const Point& p) {
                    double min_dist = p.DistanceSquare(local_centers[0]);
                    size_t cluster_id = 0;

                    for (size_t i = 1; i < local_centers.size(); ++i) {
                        double dist = p.DistanceSquare(local_centers[i]);
                        if (dist < min_dist)
                            min_dist = dist, cluster_id = i;
                    }
                    return ClosestCenter { cluster_id, p, /* count */ 1 };
                })
            // new centers as the mean of all points associated with it
            .ReduceByKey(
                // key extractor: the cluster id
                [](const ClosestCenter& cc) { return cc.cluster_id; },
                // reduction: add points and the counter
                [](const ClosestCenter& a, const ClosestCenter& b) {
                    return ClosestCenter {
                        a.cluster_id, a.point + b.point, a.count + b.count
                    };
                })
            .Map([](const ClosestCenter& cc) {
                     return cc.point / cc.count;
                 });

        new_centers.Print("new_centers");

        // Collapse() is needed to fold lambda chain to DIA<Points>
        centers = new_centers.Collapse();
    }

    centers.Print("final centers");

    return centers.AllGather();
    //! [step4 iteration loop]
}

int main() {
    // launch Thrill program: the lambda function will be run on each worker.
    return thrill::Run(
        [&](thrill::Context& ctx) { Process(ctx); });
}

/******************************************************************************/