kmeans++/KMeans.cpp

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   |                     Mobile Robot Programming Toolkit (MRPT)            |
   |                          https://www.mrpt.org/                         |
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   | Copyright (c) 2005-2019, Individual contributors, see AUTHORS file     |
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   +------------------------------------------------------------------------+ */
// See KMeans.h
//
// Author: David Arthur (darthur@gmail.com), 2009

// Includes
#include "KMeans.h"
#include <ctime>
#include <sstream>
#include <vector>
#include "KmTree.h"
using namespace std;

// Logging
static vector<ostream*> gLogOutputs;
static vector<ostream*> gVerboseLogOutputs;
#define LOG(verbose, text)                                \
    {                                                     \
        vector<ostream*>& outputs =                       \
            (verbose ? gVerboseLogOutputs : gLogOutputs); \
        if (outputs.size() > 0)                           \
        {                                                 \
            ostringstream string_stream;                  \
            string_stream << text;                        \
            for (int i = 0; i < (int)outputs.size(); i++) \
                *(outputs[i]) << string_stream.str();     \
        }                                                 \
    }
void AddKMeansLogging(std::ostream* out, bool verbose)
{
    if (verbose) gVerboseLogOutputs.push_back(out);
    gLogOutputs.push_back(out);
}
void ClearKMeansLogging()
{
    gLogOutputs.clear();
    gVerboseLogOutputs.clear();
}

// Returns the number of seconds since the program began execution.
static double GetSeconds() { return double(clock()) / CLOCKS_PER_SEC; }
// See KMeans.h
// Performs one full execution of k-means, logging any relevant information, and
// tracking meta
// statistics for the run. If min or max values are negative, they are treated
// as unset.
// best_centers and best_assignment can be 0, in which case they are not set.
static void RunKMeansOnce(
    const KmTree& tree, int n, int k, int d, Scalar* points, Scalar* centers,
    Scalar* min_cost, Scalar* max_cost, Scalar* total_cost, double start_time,
    double* min_time, double* max_time, double* total_time,
    Scalar* best_centers, int* best_assignment)
{
    (void)(n);
    (void)(points);
    const auto kEpsilon =
        Scalar(1e-8);  // Used to determine when to terminate k-means

    // Do iterations of k-means until the cost stabilizes
    Scalar old_cost = 0;
    bool is_done = false;
    for (int iteration = 0; !is_done; iteration++)
    {
        Scalar new_cost = tree.DoKMeansStep(k, centers, nullptr);
        is_done = (iteration > 0 && new_cost >= (1 - kEpsilon) * old_cost);
        old_cost = new_cost;
        LOG(true, "Completed iteration #" << (iteration + 1) << ", cost="
                                          << new_cost << "..." << endl);
    }
    double this_time = GetSeconds() - start_time;

    // Log the clustering we found
    LOG(false, "Completed run: cost=" << old_cost << " (" << this_time
                                      << " seconds)" << endl);

    // Handle a new min cost, updating best_centers and best_assignment as
    // appropriate
    if (*min_cost < 0 || old_cost < *min_cost)
    {
        *min_cost = old_cost;
        if (best_assignment != nullptr)
            tree.DoKMeansStep(k, centers, best_assignment);
        if (best_centers != nullptr)
            memcpy(best_centers, centers, sizeof(Scalar) * k * d);
    }

    // Update all other aggregate stats
    if (*max_cost < old_cost) *max_cost = old_cost;
    *total_cost += old_cost;
    if (*min_time < 0 || *min_time > this_time) *min_time = this_time;
    if (*max_time < this_time) *max_time = this_time;
    *total_time += this_time;
}

// Outputs all meta-stats for a set of k-means or k-means++ runs.
void LogMetaStats(
    Scalar min_cost, Scalar max_cost, Scalar total_cost, double min_time,
    double max_time, double total_time, int num_attempts)
{
    LOG(false, "Aggregate info over " << num_attempts << " runs:" << endl);
    LOG(false, "  Cost: min=" << min_cost
                              << " average=" << (total_cost / num_attempts)
                              << " max=" << max_cost << endl);
    LOG(false, "  Time: min=" << min_time
                              << " average=" << (total_time / num_attempts)
                              << " max=" << max_time << endl
                              << endl);
}

// See KMeans.h
Scalar RunKMeans(
    int n, int k, int d, Scalar* points, int attempts, Scalar* ret_centers,
    int* ret_assignment)
{
    KM_ASSERT(k >= 1);

    // Create the tree and log
    LOG(false, "Running k-means..." << endl);
    KmTree tree(n, d, points);
    LOG(false, "Done preprocessing..." << endl);

    // Initialization
    auto* centers = (Scalar*)malloc(sizeof(Scalar) * k * d);
    int* unused_centers = (int*)malloc(sizeof(int) * n);
    KM_ASSERT(centers != nullptr && unused_centers != nullptr);
    Scalar min_cost = -1, max_cost = -1, total_cost = 0;
    double min_time = -1, max_time = -1, total_time = 0;

    // Handle k > n
    if (k > n)
    {
        memset(centers + n * d, -1, (k - d) * sizeof(Scalar));
        k = n;
    }

    // Run all the attempts
    for (int attempt = 0; attempt < attempts; attempt++)
    {
        double start_time = GetSeconds();

        // Choose centers uniformly at random
        for (int i = 0; i < n; i++) unused_centers[i] = i;
        int num_unused_centers = n;
        for (int i = 0; i < k; i++)
        {
            int j = GetRandom(num_unused_centers--);
            memcpy(
                centers + i * d, points + unused_centers[j] * d,
                d * sizeof(Scalar));
            unused_centers[j] = unused_centers[num_unused_centers];
        }

        // Run k-means
        RunKMeansOnce(
            tree, n, k, d, points, centers, &min_cost, &max_cost, &total_cost,
            start_time, &min_time, &max_time, &total_time, ret_centers,
            ret_assignment);
    }
    LogMetaStats(
        min_cost, max_cost, total_cost, min_time, max_time, total_time,
        attempts);

    // Clean up and return
    free(unused_centers);
    free(centers);
    return min_cost;
}

// See KMeans.h
Scalar RunKMeansPlusPlus(
    int n, int k, int d, Scalar* points, int attempts, Scalar* ret_centers,
    int* ret_assignment)
{
    KM_ASSERT(k >= 1);

    // Create the tree and log
    LOG(false, "Running k-means++..." << endl);
    KmTree tree(n, d, points);
    LOG(false, "Done preprocessing..." << endl);

    // Initialization
    auto* centers = (Scalar*)malloc(sizeof(Scalar) * k * d);
    KM_ASSERT(centers != nullptr);
    Scalar min_cost = -1, max_cost = -1, total_cost = 0;
    double min_time = -1, max_time = -1, total_time = 0;

    // Run all the attempts
    for (int attempt = 0; attempt < attempts; attempt++)
    {
        double start_time = GetSeconds();

        // Choose centers using k-means++ seeding
        tree.SeedKMeansPlusPlus(k, centers);

        // Run k-means
        RunKMeansOnce(
            tree, n, k, d, points, centers, &min_cost, &max_cost, &total_cost,
            start_time, &min_time, &max_time, &total_time, ret_centers,
            ret_assignment);
    }
    LogMetaStats(
        min_cost, max_cost, total_cost, min_time, max_time, total_time,
        attempts);

    // Clean up and return
    free(centers);
    return min_cost;
}