Line data    Source code

       1             : // The libMesh Finite Element Library.
       2             : // Copyright (C) 2002-2025 Benjamin S. Kirk, John W. Peterson, Roy H. Stogner
       3             : 
       4             : // This library is free software; you can redistribute it and/or
       5             : // modify it under the terms of the GNU Lesser General Public
       6             : // License as published by the Free Software Foundation; either
       7             : // version 2.1 of the License, or (at your option) any later version.
       8             : 
       9             : // This library is distributed in the hope that it will be useful,
      10             : // but WITHOUT ANY WARRANTY; without even the implied warranty of
      11             : // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
      12             : // Lesser General Public License for more details.
      13             : 
      14             : // You should have received a copy of the GNU Lesser General Public
      15             : // License along with this library; if not, write to the Free Software
      16             : // Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
      17             : 
      18             : 
      19             : 
      20             : #ifndef LIBMESH_STATISTICS_H
      21             : #define LIBMESH_STATISTICS_H
      22             : 
      23             : // Local includes
      24             : #include "libmesh/libmesh_common.h"
      25             : #include "libmesh/id_types.h"
      26             : 
      27             : // C++ includes
      28             : #include <vector>
      29             : #include <cstdlib> // *must* precede <cmath> for proper std:abs() on PGI, Sun Studio CC
      30             : #include <cmath>
      31             : 
      32             : namespace libMesh
      33             : {
      34             : 
      35             : /**
      36             :  * The StatisticsVector class is derived from the std::vector<> and
      37             :  * therefore has all of its useful features.  It was designed to not
      38             :  * have any internal state, i.e. no public or private data members.
      39             :  * Also, it was only designed for classes and types for which the
      40             :  * operators +,*,/ have meaning, specifically floats, doubles, ints,
      41             :  * etc.  The main reason for this design decision was to allow a
      42             :  * std::vector<> to be successfully cast to a StatisticsVector,
      43             :  * thereby enabling its additional functionality.  We do not
      44             :  * anticipate any problems with deriving from an stl container which
      45             :  * lacks a virtual destructor in this case.
      46             :  *
      47             :  * Where manipulation of the data set was necessary (for example
      48             :  * sorting) two versions of member functions have been implemented.
      49             :  * The non-const versions perform sorting directly in the data set,
      50             :  * invalidating pointers and changing the entries.  const versions of
      51             :  * the same functions are generally available, and will be
      52             :  * automatically invoked on const StatisticsVector objects.  A
      53             :  * draw-back to the const versions is that they simply make a copy of
      54             :  * the original object and therefore double the original memory
      55             :  * requirement for the data set.
      56             :  *
      57             :  * Most of the actual code was copied or adapted from the GNU
      58             :  * Scientific Library (GSL). More precisely, the recursion relations
      59             :  * for computing the mean were implemented in order to avoid possible
      60             :  * problems with buffer overruns.
      61             :  *
      62             :  * \author John W. Peterson
      63             :  * \date 2002
      64             :  * \brief A std::vector derived class for implementing simple statistical algorithms.
      65             :  */
      66             : template <typename T>
      67           0 : class StatisticsVector : public std::vector<T>
      68             : {
      69             : public:
      70             : 
      71             :   /**
      72             :    * Call the std::vector constructor.
      73             :    */
      74             :   explicit
      75       56553 :   StatisticsVector(dof_id_type i=0) : std::vector<T> (i) {}
      76             : 
      77             :   /**
      78             :    * Call the std::vector constructor, fill each entry with \p val.
      79             :    */
      80           0 :   StatisticsVector(dof_id_type i, T val) : std::vector<T> (i,val) {}
      81             : 
      82             :   /**
      83             :    * Destructor.  Virtual so we can derive from the \p StatisticsVector
      84             :    */
      85       29729 :   virtual ~StatisticsVector () = default;
      86             : 
      87             : 
      88             :   /**
      89             :    * \returns The l2 norm of the data set.
      90             :    */
      91             :   virtual Real l2_norm() const;
      92             : 
      93             :   /**
      94             :    * \returns The minimum value in the data set.
      95             :    */
      96             :   virtual T minimum() const;
      97             : 
      98             :   /**
      99             :    * \returns The maximum value in the data set.
     100             :    */
     101             :   virtual T maximum() const;
     102             : 
     103             :   /**
     104             :    * \returns The mean value of the data set using a recurrence
     105             :    * relation.
     106             :    *
     107             :    * Source: GNU Scientific Library
     108             :    */
     109             :   virtual Real mean() const;
     110             : 
     111             :   /**
     112             :    * \returns The median (e.g. the middle) value of the data set.
     113             :    *
     114             :    * This function modifies the original data by sorting, so it can't
     115             :    * be called on const objects.  Source: GNU Scientific Library.
     116             :    */
     117             :   virtual Real median();
     118             : 
     119             :   /**
     120             :    * A const version of the median function.  Requires twice the memory
     121             :    * of original data set but does not change the original.
     122             :    */
     123             :   virtual Real median() const;
     124             : 
     125             :   /**
     126             :    * \returns The variance of the data set.
     127             :    *
     128             :    * Uses a recurrence relation to prevent data overflow for large
     129             :    * sums.
     130             :    *
     131             :    * \note The variance is equal to the standard deviation squared.
     132             :    * Source: GNU Scientific Library.
     133             :    */
     134           0 :   virtual Real variance() const
     135           0 :   { return this->variance(this->mean()); }
     136             : 
     137             :   /**
     138             :    * \returns The variance of the data set where the \p mean is
     139             :    * provided.
     140             :    *
     141             :    * This is useful for efficiency when you have already calculated
     142             :    * the mean. Uses a recurrence relation to prevent data overflow for
     143             :    * large sums.
     144             :    *
     145             :    * \note The variance is equal to the standard deviation squared.
     146             :    * Source: GNU Scientific Library.
     147             :    */
     148             :   virtual Real variance(const Real known_mean) const;
     149             : 
     150             :   /**
     151             :    * \returns The standard deviation of the data set, which is simply
     152             :    * the square-root of the variance.
     153             :    */
     154           0 :   virtual Real stddev() const
     155           0 :   { return std::sqrt(this->variance()); }
     156             : 
     157             :   /**
     158             :    * \returns Computes the standard deviation of the data set, which
     159             :    * is simply the square-root of the variance.
     160             :    *
     161             :    * This method can be used for efficiency when the \p mean has
     162             :    * already been computed.
     163             :    */
     164           0 :   virtual Real stddev(const Real known_mean) const
     165           0 :   { return std::sqrt(this->variance(known_mean)); }
     166             : 
     167             :   /**
     168             :    * Divides all entries by the largest entry and stores the result.
     169             :    */
     170             :   void normalize();
     171             : 
     172             :   /**
     173             :    * \returns A histogram with n_bins bins for the data set.
     174             :    *
     175             :    * For simplicity, the bins are assumed to be of uniform size.  Upon
     176             :    * return, the bin_members vector will contain unsigned integers
     177             :    * which give the number of members in each bin.  WARNING: This
     178             :    * non-const function sorts the vector, changing its order.  Source:
     179             :    * GNU Scientific Library.
     180             :    */
     181             :   virtual void histogram (std::vector<dof_id_type> & bin_members,
     182             :                           unsigned int n_bins=10);
     183             : 
     184             :   /**
     185             :    * Generates a Matlab/Octave style file which can be used to
     186             :    * make a plot of the histogram having the desired number of bins.
     187             :    * Uses the histogram(...) function in this class
     188             :    * WARNING: The histogram(...) function is non-const, and changes
     189             :    * the order of the vector.
     190             :    */
     191             :   void plot_histogram(const processor_id_type my_procid,
     192             :                       const std::string & filename,
     193             :                       unsigned int n_bins);
     194             : 
     195             :   /**
     196             :    * A const version of the histogram function.
     197             :    */
     198             :   virtual void histogram (std::vector<dof_id_type> & bin_members,
     199             :                           unsigned int n_bins=10) const;
     200             : 
     201             :   /**
     202             :    * \returns A vector of dof_id_types which corresponds
     203             :    * to the indices of every member of the data set
     204             :    * below the cutoff value "cut".
     205             :    */
     206             :   virtual std::vector<dof_id_type> cut_below(Real cut) const;
     207             : 
     208             :   /**
     209             :    * \returns A vector of dof_id_types which corresponds to the
     210             :    * indices of every member of the data set above the cutoff value
     211             :    * cut.
     212             :    *
     213             :    * I chose not to combine these two functions since the interface is
     214             :    * cleaner with one passed parameter instead of two.
     215             :    */
     216             :   virtual std::vector<dof_id_type> cut_above(Real cut) const;
     217             : };
     218             : 
     219             : } // namespace libMesh
     220             : 
     221             : #endif // LIBMESH_STATISTICS_H