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https://github.com/GoldenCheetah/GoldenCheetah.git
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1dc1cd678f
.. with grateful thanks to Greg Hamerly A fast kmeans algorithm described here: https://epubs.siam.org/doi/10.1137/1.9781611972801.12 The source repository is also here: https://github.com/ghamerly/fast-kmeans NOTE: The original source has been included largely as-is with a view to writing a wrapper around it using Qt semantics for use in GoldenCheetah (e.g. via datafilter) The original source included multiple kmeans algorithms we have only kept the `fast' Hamerly variant.
71 lines
1.6 KiB
C++
71 lines
1.6 KiB
C++
#include <iostream>
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#include <fstream>
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#include <string>
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#include <cassert>
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#include "kmeans/general_functions.h"
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#include "kmeans/kmeans.h"
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#include "dataset.h"
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#include "hamerly_kmeans.h"
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Dataset *load_dataset(std::string const &filename) {
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std::ifstream input(filename.c_str());
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int n, d;
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input >> n >> d;
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Dataset *x = new Dataset(n, d);
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for (int i = 0; i < n * d; ++i) input >> x->data[i];
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return x;
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}
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Kmeans *get_algorithm(std::string const &name) {
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if (name == "hamerly") return new HamerlyKmeans();
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return NULL;
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}
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int main(int argc, char **argv) {
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if (argc != 5) {
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std::cout << "usage: " << argv[0] << " algorithm dataset k [centers|assignment]\n";
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return 1;
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}
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std::string algorithm_name(argv[1]);
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std::string filename(argv[2]);
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int k = std::stoi(argv[3]);
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std::string output(argv[4]);
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Dataset *x = load_dataset(filename);
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Kmeans *algorithm = get_algorithm(algorithm_name);
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Dataset *initialCenters = init_centers_kmeanspp_v2(*x, k);
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unsigned short *assignment = new unsigned short[x->n];
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assign(*x, *initialCenters, assignment);
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algorithm->initialize(x, k, assignment, 1);
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algorithm->run(10000);
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Dataset const *finalCenters = algorithm->getCenters();
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if (output == "centers") {
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finalCenters->print();
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} else {
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assign(*x, *finalCenters, assignment);
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for (int i = 0; i < x->n; ++i) {
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std::cout << assignment[i] << "\n";
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}
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}
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delete x;
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delete algorithm;
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delete initialCenters;
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delete [] assignment;
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return 0;
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}
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