Point Cloud Library (PCL)  1.14.1-dev
vector_average.h
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37 
38 #pragma once
39 
40 #include <Eigen/Core> // for Matrix
41 
42 #include <pcl/memory.h>
43 #include <pcl/pcl_macros.h>
44 
45 namespace pcl
46 {
47  /** \brief Calculates the weighted average and the covariance matrix
48  *
49  * A class to calculate the weighted average and the covariance matrix of a set of vectors with given weights.
50  * The original data is not saved. Mean and covariance are calculated iteratively.
51  * \author Bastian Steder
52  * \ingroup common
53  */
54  template <typename real, int dimension>
56  {
57  public:
58  using VectorType = Eigen::Matrix<real, dimension, 1>;
59  using MatrixType = Eigen::Matrix<real, dimension, dimension>;
60  //-----CONSTRUCTOR&DESTRUCTOR-----
61  /** Constructor - dimension gives the size of the vectors to work with. */
62  VectorAverage ();
63 
64  //-----METHODS-----
65  /** Reset the object to work with a new data set */
66  inline void
67  reset ();
68 
69  /** Get the mean of the added vectors */
70  inline const
71  VectorType& getMean () const { return mean_;}
72 
73  /** Get the covariance matrix of the added vectors */
74  inline const
75  MatrixType& getCovariance () const { return covariance_;}
76 
77  /** Get the summed up weight of all added vectors */
78  inline real
80 
81  /** Get the number of added vectors */
82  inline unsigned int
84 
85  /** Add a new sample */
86  inline void
87  add (const VectorType& sample, real weight=1.0);
88 
89  /** Do Principal component analysis */
90  inline void
91  doPCA (VectorType& eigen_values, VectorType& eigen_vector1,
92  VectorType& eigen_vector2, VectorType& eigen_vector3) const;
93 
94  /** Do Principal component analysis */
95  inline void
96  doPCA (VectorType& eigen_values) const;
97 
98  /** Get the eigenvector corresponding to the smallest eigenvalue */
99  inline void
100  getEigenVector1 (VectorType& eigen_vector1) const;
101 
103 
104  //-----VARIABLES-----
105 
106 
107  protected:
108  //-----METHODS-----
109  //-----VARIABLES-----
110  unsigned int noOfSamples_ = 0;
112  VectorType mean_ = VectorType::Identity ();
113  MatrixType covariance_ = MatrixType::Identity ();
114  };
115 
119 } // END namespace
120 
121 #include <pcl/common/impl/vector_average.hpp>
Calculates the weighted average and the covariance matrix.
void add(const VectorType &sample, real weight=1.0)
Add a new sample.
void reset()
Reset the object to work with a new data set.
VectorAverage()
Constructor - dimension gives the size of the vectors to work with.
Eigen::Matrix< real, dimension, 1 > VectorType
const MatrixType & getCovariance() const
Get the covariance matrix of the added vectors.
void doPCA(VectorType &eigen_values, VectorType &eigen_vector1, VectorType &eigen_vector2, VectorType &eigen_vector3) const
Do Principal component analysis.
Eigen::Matrix< real, dimension, dimension > MatrixType
real getAccumulatedWeight() const
Get the summed up weight of all added vectors.
const VectorType & getMean() const
Get the mean of the added vectors.
void getEigenVector1(VectorType &eigen_vector1) const
Get the eigenvector corresponding to the smallest eigenvalue.
unsigned int getNoOfSamples()
Get the number of added vectors.
MatrixType covariance_
unsigned int noOfSamples_
#define PCL_MAKE_ALIGNED_OPERATOR_NEW
Macro to signal a class requires a custom allocator.
Definition: memory.h:63
Defines functions, macros and traits for allocating and using memory.
Defines all the PCL and non-PCL macros used.