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 *  Point Cloud Library (PCL) - www.pointclouds.org
 *  Copyright (c) 2010-2012, Willow Garage, Inc.
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#pragma once

#include <Eigen/Core> // for Matrix

#include <pcl/memory.h>
#include <pcl/pcl_macros.h>

namespace pcl 
{
  /** \brief Calculates the weighted average and the covariance matrix
    *
    * A class to calculate the weighted average and the covariance matrix of a set of vectors with given weights.
    * The original data is not saved. Mean and covariance are calculated iteratively.
    * \author Bastian Steder
    * \ingroup common
    */
  template <typename real, int dimension>
  class VectorAverage
  {
     public:
         using VectorType = Eigen::Matrix<real, dimension, 1>;
         using MatrixType = Eigen::Matrix<real, dimension, dimension>;
        //-----CONSTRUCTOR&DESTRUCTOR-----
        /** Constructor - dimension gives the size of the vectors to work with. */
        VectorAverage ();

        //-----METHODS-----
        /** Reset the object to work with a new data set */
        inline void 
        reset ();
        
        /** Get the mean of the added vectors */
        inline const
        VectorType& getMean () const { return mean_;}
        
        /** Get the covariance matrix of the added vectors */
        inline const
        MatrixType& getCovariance () const { return covariance_;}
        
        /** Get the summed up weight of all added vectors */
        inline real
        getAccumulatedWeight () const { return accumulatedWeight_;}
        
        /** Get the number of added vectors */
        inline unsigned int
        getNoOfSamples () { return noOfSamples_;}
        
        /** Add a new sample */
        inline void
        add (const VectorType& sample, real weight=1.0);

        /** Do Principal component analysis */
        inline void
        doPCA (VectorType& eigen_values, VectorType& eigen_vector1,
               VectorType& eigen_vector2, VectorType& eigen_vector3) const;
        
        /** Do Principal component analysis */
        inline void
        doPCA (VectorType& eigen_values) const;
        
        /** Get the eigenvector corresponding to the smallest eigenvalue */
        inline void
        getEigenVector1 (VectorType& eigen_vector1) const;

        PCL_MAKE_ALIGNED_OPERATOR_NEW
        
        //-----VARIABLES-----

        
     protected:
        //-----METHODS-----
        //-----VARIABLES-----
        unsigned int noOfSamples_ = 0;
        real accumulatedWeight_ = 0;
        VectorType mean_ = VectorType::Identity ();
        MatrixType covariance_ = MatrixType::Identity ();
  };

  using VectorAverage2f = VectorAverage<float, 2>;
  using VectorAverage3f = VectorAverage<float, 3>;
  using VectorAverage4f = VectorAverage<float, 4>;
}  // END namespace

#include <pcl/common/impl/vector_average.hpp>