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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.
55 lines
2.1 KiB
C++
55 lines
2.1 KiB
C++
#ifndef ORIGINAL_SPACE_KMEANS_H
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#define ORIGINAL_SPACE_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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* OriginalSpaceKmeans is a base class for other algorithms that operate in the
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* same space as the data being clustered (as opposed to kernelized k-means
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* algorithms, which operate in kernel space).
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*/
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#include "kmeans.h"
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/* Cluster with the cluster centers living in the original space (with the
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* data). This is as opposed to a kernelized version of k-means, where the
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* center points might not be explicitly represented. This is also an abstract
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* class.
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*/
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class OriginalSpaceKmeans : public Kmeans {
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public:
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OriginalSpaceKmeans();
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virtual ~OriginalSpaceKmeans() { free(); }
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virtual void free();
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virtual void initialize(Dataset const *aX, unsigned short aK, unsigned short *initialAssignment, int aNumThreads);
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virtual double pointPointInnerProduct(int x1ndx, int x2ndx) const;
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virtual double pointCenterInnerProduct(int xndx, unsigned short cndx) const;
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virtual double centerCenterInnerProduct(unsigned short c1ndx, unsigned short c2ndx) const;
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virtual Dataset const *getCenters() const { return centers; }
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protected:
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// Move the centers to the average of their current assigned points,
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// compute the distance moved by each center, and return the index of
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// the furthest-moving center.
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int move_centers();
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virtual void changeAssignment(int xIndex, int closestCluster, int threadId);
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// The set of centers we are operating on.
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Dataset *centers;
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// sumNewCenters and centerCount provide sufficient statistics to
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// quickly calculate the changing locations of the centers. Whenever a
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// point changes cluster membership, we subtract (add) it from (to) the
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// row in sumNewCenters associated with its old (new) cluster. We also
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// decrement (increment) centerCount for the old (new) cluster.
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Dataset **sumNewCenters;
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};
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#endif
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