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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.
80 lines
2.5 KiB
C++
80 lines
2.5 KiB
C++
/* Authors: Greg Hamerly and Jonathan Drake
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* Feedback: hamerly@cs.baylor.edu
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* See: http://cs.baylor.edu/~hamerly/software/kmeans.php
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* Copyright 2014
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*/
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#include "triangle_inequality_base_kmeans.h"
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#include "kmeans_general_functions.h"
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#include <cassert>
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#include <limits>
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#include <cmath>
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void TriangleInequalityBaseKmeans::free() {
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OriginalSpaceKmeans::free();
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delete [] s;
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delete [] upper;
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delete [] lower;
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s = NULL;
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upper = NULL;
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lower = NULL;
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}
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/* This function computes the inter-center distances, keeping only the closest
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* distances, and updates "s". After this, s[j] will contain the distance
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* between center j and its closest other center, divided by two. The division
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* here saves repeated work later, since we always will need the distance / 2.
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*
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* Parameters: none
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*
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* Return value: none
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*/
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// TODO: parallelize this
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void TriangleInequalityBaseKmeans::update_s(int threadId) {
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// initialize
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for (int c1 = 0; c1 < k; ++c1) {
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if (c1 % numThreads == threadId) {
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s[c1] = std::numeric_limits<double>::max();
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}
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}
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// compute inter-center squared distances between all pairs
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for (int c1 = 0; c1 < k; ++c1) {
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if (c1 % numThreads == threadId) {
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for (int c2 = 0; c2 < k; ++c2) {
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if (c2 == c1) {
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continue;
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}
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double d2 = centerCenterDist2(c1, c2);
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if (d2 < s[c1]) { s[c1] = d2; }
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}
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// take the root and divide by two
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s[c1] = sqrt(s[c1]) / 2.0;
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}
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}
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}
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/* This function initializes the upper/lower bounds, assignment, centerCounts,
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* and sumNewCenters. It sets the bounds to invalid values which will force the
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* first iteration of k-means to set them correctly. NB: subclasses should set
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* numLowerBounds appropriately before entering this function.
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*
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* Parameters: none
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*
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* Return value: none
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*/
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void TriangleInequalityBaseKmeans::initialize(Dataset const *aX, unsigned short aK, unsigned short *initialAssignment, int aNumThreads) {
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OriginalSpaceKmeans::initialize(aX, aK, initialAssignment, aNumThreads);
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s = new double[k];
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upper = new double[n];
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lower = new double[n * numLowerBounds];
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// start with invalid bounds and assignments which will force the first
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// iteration of k-means to do all its standard work
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std::fill(s, s + k, 0.0);
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std::fill(upper, upper + n, std::numeric_limits<double>::max());
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std::fill(lower, lower + n * numLowerBounds, 0.0);
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}
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