Loading...
Searching...
No Matches
GreedyKCenters.h
1/*********************************************************************
2 * Software License Agreement (BSD License)
3 *
4 * Copyright (c) 2011, Rice University
5 * All rights reserved.
6 *
7 * Redistribution and use in source and binary forms, with or without
8 * modification, are permitted provided that the following conditions
9 * are met:
10 *
11 * * Redistributions of source code must retain the above copyright
12 * notice, this list of conditions and the following disclaimer.
13 * * Redistributions in binary form must reproduce the above
14 * copyright notice, this list of conditions and the following
15 * disclaimer in the documentation and/or other materials provided
16 * with the distribution.
17 * * Neither the name of the Rice University nor the names of its
18 * contributors may be used to endorse or promote products derived
19 * from this software without specific prior written permission.
20 *
21 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
22 * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
23 * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
24 * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
25 * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
26 * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
27 * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
28 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
29 * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
30 * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
31 * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
32 * POSSIBILITY OF SUCH DAMAGE.
33 *********************************************************************/
34
35/* Author: Mark Moll */
36
37#ifndef OMPL_DATASTRUCTURES_GREEDY_K_CENTERS_
38#define OMPL_DATASTRUCTURES_GREEDY_K_CENTERS_
39
40#include "ompl/util/RandomNumbers.h"
41#include <functional>
42#include <Eigen/Core>
43
44namespace ompl
45{
49 template <typename _T>
51 {
52 public:
54 using DistanceFunction = std::function<double(const _T &, const _T &)>;
56 using Matrix = Eigen::MatrixXd;
57
58 GreedyKCenters() = default;
59
60 virtual ~GreedyKCenters() = default;
61
64 {
65 distFun_ = distFun;
66 }
67
70 {
71 return distFun_;
72 }
73
82 void kcenters(const std::vector<_T> &data, unsigned int k, std::vector<unsigned int> &centers, Matrix &dists)
83 {
84 // array containing the minimum distance between each data point
85 // and the centers computed so far
86 std::vector<double> minDist(data.size(), std::numeric_limits<double>::infinity());
87
88 centers.clear();
89 centers.reserve(k);
90 if ((std::size_t)dists.rows() < data.size() || (std::size_t)dists.cols() < k)
91 dists.resize(std::max(2u * (std::size_t)dists.rows() + 1u, data.size()), k);
92 // first center is picked randomly
93 centers.push_back(rng_.uniformInt(0, data.size() - 1));
94 for (unsigned i = 1; i < k; ++i)
95 {
96 unsigned ind = 0;
97 const _T &center = data[centers[i - 1]];
98 double maxDist = -std::numeric_limits<double>::infinity();
99 for (unsigned j = 0; j < data.size(); ++j)
100 {
101 if ((dists(j, i - 1) = distFun_(data[j], center)) < minDist[j])
102 minDist[j] = dists(j, i - 1);
103 // the j-th center is the one furthest away from center 0,..,j-1
104 if (minDist[j] > maxDist)
105 {
106 ind = j;
107 maxDist = minDist[j];
108 }
109 }
110 // no more centers available
111 if (maxDist < std::numeric_limits<double>::epsilon())
112 break;
113 centers.push_back(ind);
114 }
115
116 const _T &center = data[centers.back()];
117 unsigned i = centers.size() - 1;
118 for (unsigned j = 0; j < data.size(); ++j)
119 dists(j, i) = distFun_(data[j], center);
120 }
121
122 protected:
125
128 };
129} // namespace ompl
130
131#endif
An instance of this class can be used to greedily select a given number of representatives from a set...
DistanceFunction distFun_
The used distance function.
std::function< double(const _T &, const _T &)> DistanceFunction
The definition of a distance function.
void setDistanceFunction(const DistanceFunction &distFun)
Set the distance function to use.
const DistanceFunction & getDistanceFunction() const
Get the distance function used.
void kcenters(const std::vector< _T > &data, unsigned int k, std::vector< unsigned int > &centers, Matrix &dists)
Greedy algorithm for selecting k centers.
Eigen::MatrixXd Matrix
A matrix type for storing distances between points and centers.
Random number generation. An instance of this class cannot be used by multiple threads at once (membe...
int uniformInt(int lower_bound, int upper_bound)
Generate a random integer within given bounds: [lower_bound, upper_bound].
Main namespace. Contains everything in this library.