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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.
86 lines
3.2 KiB
C++
86 lines
3.2 KiB
C++
#ifndef DATASET_H
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#define DATASET_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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* A Dataset class represents a collection of multidimensional records, as is
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* typical in metric machine learning. Every record has the same number of
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* dimensions (values), and every value must be numeric. Undefined values are
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* not allowed.
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*
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* This particular implementation keeps all the data in a 1-dimensional array,
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* and also optionally keeps extra storage for the sum of the squared values of
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* each record. However, the Dataset class does NOT automatically populate or
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* update the sumDataSquared values.
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*/
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#include <cstddef>
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#include <iostream>
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class Dataset {
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public:
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// default constructor -- constructs a completely empty dataset with no
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// records
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Dataset() : n(0), d(0), nd(0), data(NULL), sumDataSquared(NULL) {}
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// construct a dataset of a particular size, and determine whether to
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// keep the sumDataSquared
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Dataset(int aN, int aD, bool keepSDS = false) : n(aN), d(aD), nd(n * d),
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data(new double[nd]),
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sumDataSquared(keepSDS ? new double[n] : NULL) {}
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// copy constructor -- makes a deep copy of everything in x
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Dataset(Dataset const &x);
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// destroys the dataset safely
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~Dataset() {
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n = d = nd = 0;
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double *dp = data, *sdsp = sumDataSquared;
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data = sumDataSquared = NULL;
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delete [] dp;
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delete [] sdsp;
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}
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// operator= is the standard deep-copy assignment operator, which
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// returns a const reference to *this.
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Dataset const &operator=(Dataset const &x);
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// allows modification of the record ndx and dimension dim
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double &operator()(int ndx, int dim);
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// allows const access to record ndx and dimension dim
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const double &operator()(int ndx, int dim) const;
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// fill the entire dataset with value. Does NOT update sumDataSquared.
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void fill(double value);
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// print the dataset to standard output (cout), using formatting to keep the
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// data in matrix format
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void print(std::ostream &out = std::cout) const;
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// n represents the number of records
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// d represents the dimension
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// nd is a shortcut for the value n * d
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int n, d, nd;
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// data is an array of length n*d that stores all of the records in
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// record-major (row-major) order. Thus data[0]...data[d-1] are the
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// values associated with the first record.
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double *data;
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// sumDataSquared is an (optional) sum of squared values for every
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// record. Thus,
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// sumDataSquared[0] = data[0]^2 + data[1]^2 + ... + data[d-1]^2
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// sumDataSquared[1] = data[d]^2 + data[d+1]^2 + ... + data[2*d-1]^2
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// and so on. Note that this is the *intended* use of the sumDataSquared
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// field, but that the Dataset class does NOT automatically populate or
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// update the values in sumDataSquared.
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double *sumDataSquared;
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};
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#endif
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