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.. 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.
44 lines
1.4 KiB
C++
44 lines
1.4 KiB
C++
#ifndef TRIANGLE_INEQUALITY_BASE_KMEANS_H
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#define TRIANGLE_INEQUALITY_BASE_KMEANS_H
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/* 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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* This class is an abstract base class for several other algorithms that use
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* upper & lower bounds to avoid distance calculations in k-means.
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*/
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#include "original_space_kmeans.h"
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class TriangleInequalityBaseKmeans : public OriginalSpaceKmeans {
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public:
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TriangleInequalityBaseKmeans() : numLowerBounds(0), s(NULL), upper(NULL), lower(NULL) {}
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virtual ~TriangleInequalityBaseKmeans() { free(); }
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virtual void initialize(Dataset const *aX, unsigned short aK, unsigned short *initialAssignment, int aNumThreads);
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virtual void free();
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protected:
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void update_s(int threadId);
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// The number of lower bounds being used by this algorithm.
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int numLowerBounds;
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// Half the distance between each center and its closest other center.
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double *s;
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// One upper bound for each point on the distance between that point and
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// its assigned (closest) center.
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double *upper;
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// Lower bound(s) for each point on the distance between that point and
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// the centers being tracked for lower bounds, which may be 1 to k.
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// Actual size is n * numLowerBounds.
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double *lower;
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};
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#endif
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