Point Cloud Library (PCL)  1.11.1-dev
kmeans.h
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35  * Author : Christian Potthast
36  * Email : potthast@usc.edu
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39 
40 #pragma once
41 
42 #include <pcl/memory.h>
43 #include <pcl/pcl_macros.h>
44 
45 #include <set>
46 #include <vector> // for vector
47 
48 namespace pcl {
49 
50 /** K-means clustering.
51  *
52  * \author Christian Potthast
53  * \ingroup ML
54  */
56 public:
57  using PointId = unsigned int; // the id of this point
58  using ClusterId = unsigned int; // the id of this cluster
59 
60  // using Point = std::vector<Coord>; // a point (a centroid)
61 
62  using SetPoints = std::set<PointId>; // set of points
63 
64  using Point = std::vector<float>;
65 
66  // ClusterId -> (PointId, PointId, PointId, .... )
67  using ClustersToPoints = std::vector<SetPoints>;
68  // PointId -> ClusterId
69  using PointsToClusters = std::vector<ClusterId>;
70  // coll of centroids
71  using Centroids = std::vector<Point>;
72 
73  /** Empty constructor. */
74  Kmeans(unsigned int num_points, unsigned int num_dimensions);
75 
76  /** This destructor destroys. */
77  ~Kmeans();
78 
79  /** This method sets the k-means cluster size.
80  *
81  * \param[in] k number of clusters
82  */
83  void
84  setClusterSize(unsigned int k)
85  {
86  num_clusters_ = k;
87  };
88 
89  /*
90  void
91  setClusterField (std::string field_name)
92  {
93  cluster_field_name_ = field_name;
94  };
95  */
96 
97  // void
98  // getClusterCentroids (PointT &out);
99 
100  // void
101  // cluster (std::vector<PointIndices> &clusters);
102 
103  void
104  kMeans();
105 
106  void
107  setInputData(std::vector<Point>& data)
108  {
109  if (num_points_ != data.size())
110  std::cout << "Data vector not the same" << std::endl;
111 
112  data_ = data;
113  }
114 
115  void
116  addDataPoint(Point& data_point)
117  {
118  if (num_dimensions_ != data_point.size())
119  std::cout << "Dimensions not the same" << std::endl;
120 
121  data_.push_back(data_point);
122  }
123 
124  // Initial partition points among available clusters
125  void
126  initialClusterPoints();
127 
128  void
129  computeCentroids();
130 
131  // distance between two points
132  float
133  distance(const Point& x, const Point& y)
134  {
135  float total = 0.0;
136  float diff;
137 
138  auto cpy = y.cbegin();
139  for (auto cpx = x.cbegin(), cpx_end = x.cend(); cpx != cpx_end; ++cpx, ++cpy) {
140  diff = *cpx - *cpy;
141  total += (diff * diff);
142  }
143  return total; // no need to take sqrt, which is monotonic
144  }
145 
146  Centroids
148  {
149  return centroids_;
150  }
151 
152 protected:
153  // Members derived from the base class
154  /*
155  using BasePCLBase::input_;
156  using BasePCLBase::indices_;
157  using BasePCLBase::initCompute;
158  using BasePCLBase::deinitCompute;
159  */
160 
161  unsigned int num_points_;
162  unsigned int num_dimensions_;
163 
164  /** The number of clusters. */
165  unsigned int num_clusters_;
166 
167  /** The cluster centroids. */
168  // std::vector
169 
170  // std::string cluster_field_name_;
171 
172  // one data point
173 
174  // all data points
175  std::vector<Point> data_;
176 
180 
181 public:
183 };
184 
185 } // namespace pcl
pcl_macros.h
Defines all the PCL and non-PCL macros used.
pcl
Definition: convolution.h:46
pcl::Kmeans::Centroids
std::vector< Point > Centroids
Definition: kmeans.h:71
pcl::Kmeans::PointsToClusters
std::vector< ClusterId > PointsToClusters
Definition: kmeans.h:69
pcl::Kmeans
K-means clustering.
Definition: kmeans.h:55
pcl::Kmeans::num_points_
unsigned int num_points_
Definition: kmeans.h:161
pcl::Kmeans::Point
std::vector< float > Point
Definition: kmeans.h:64
pcl::Kmeans::centroids_
Centroids centroids_
Definition: kmeans.h:179
pcl::Kmeans::distance
float distance(const Point &x, const Point &y)
Definition: kmeans.h:133
pcl::Kmeans::get_centroids
Centroids get_centroids()
Definition: kmeans.h:147
PCL_MAKE_ALIGNED_OPERATOR_NEW
#define PCL_MAKE_ALIGNED_OPERATOR_NEW
Macro to signal a class requires a custom allocator.
Definition: memory.h:63
pcl::Kmeans::addDataPoint
void addDataPoint(Point &data_point)
Definition: kmeans.h:116
pcl::Kmeans::ClusterId
unsigned int ClusterId
Definition: kmeans.h:58
pcl::Kmeans::num_clusters_
unsigned int num_clusters_
The number of clusters.
Definition: kmeans.h:165
pcl::Kmeans::setInputData
void setInputData(std::vector< Point > &data)
Definition: kmeans.h:107
pcl::Kmeans::data_
std::vector< Point > data_
The cluster centroids.
Definition: kmeans.h:175
pcl::Kmeans::clusters_to_points_
ClustersToPoints clusters_to_points_
Definition: kmeans.h:177
pcl::Kmeans::num_dimensions_
unsigned int num_dimensions_
Definition: kmeans.h:162
memory.h
Defines functions, macros and traits for allocating and using memory.
pcl::Kmeans::PointId
unsigned int PointId
Definition: kmeans.h:57
PCL_EXPORTS
#define PCL_EXPORTS
Definition: pcl_macros.h:323
pcl::Kmeans::points_to_clusters_
PointsToClusters points_to_clusters_
Definition: kmeans.h:178
pcl::Kmeans::setClusterSize
void setClusterSize(unsigned int k)
This method sets the k-means cluster size.
Definition: kmeans.h:84
pcl::Kmeans::SetPoints
std::set< PointId > SetPoints
Definition: kmeans.h:62
pcl::Kmeans::ClustersToPoints
std::vector< SetPoints > ClustersToPoints
Definition: kmeans.h:67