/usr/include/libmesh/statistics.h is in libmesh-dev 0.7.1-2ubuntu1.
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// The libMesh Finite Element Library.
// Copyright (C) 2002-2008 Benjamin S. Kirk, John W. Peterson, Roy H. Stogner
// This library is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 2.1 of the License, or (at your option) any later version.
// This library is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
// Lesser General Public License for more details.
// You should have received a copy of the GNU Lesser General Public
// License along with this library; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#ifndef __statistics_h__
#define __statistics_h__
// C++ includes
#include <vector>
#include <cmath>
// Local includes
#include "libmesh_common.h"
namespace libMesh
{
/**
* The StatisticsVector class is derived from
* the std::vector<> and therefore has all of
* its useful features. It was designed
* to not have any internal state, i.e. no
* public or private data members. Also,
* it was only designed for classes and
* types for which the operators +,*,/ have
* meaining, specifically floats, doubles,
* ints, etc. The main reason for this design
* decision was to allow a std::vector<> to
* be successfully cast to a StatisticsVector,
* thereby enabling its additional functionality.
* We do not anticipate any problems with
* deriving from an stl container which lacks
* a virtual destructor in this case.
*
* Where manipulation of the data set was necessary
* (for example sorting) two versions of member
* functions have been implemented. The non-const
* versions perform sorting directly in the
* data set, invalidating pointers and changing
* the entries. const versions of the same
* functions are generally available, and will be
* automatically invoked on const StatisticsVector objects.
* A draw-back to the const versions is that they
* simply make a copy of the original object and
* therefore double the original memory requirement
* for the data set.
*
* Most of the actual code was copied or adapted from
* the GNU Scientific Library (GSL). More precisely,
* the recursion relations for computing the mean were
* implemented in order to avoid possible problems with
* buffer overruns.
*
* @author John W. Peterson, 2002
*/
// ------------------------------------------------------------
// StatisticsVector class definition
template <typename T>
class StatisticsVector : public std::vector<T>
{
public:
/**
* Call the std::vector constructor.
*/
StatisticsVector(unsigned int i=0) : std::vector<T> (i) {}
/**
* Call the std::vector constructor, fill each entry with \p val
*/
StatisticsVector(unsigned int i, T val) : std::vector<T> (i,val) {}
/**
* Destructor. Virtual so we can derive from the \p StatisticsVector
*/
virtual ~StatisticsVector () {}
/**
* Returns the l2 norm of the data set.
*/
virtual Real l2_norm() const;
/**
* Returns the minimum value in the data set.
*/
virtual T minimum() const;
/**
* Returns the maximum value in the data set.
*/
virtual T maximum() const;
/**
* Returns the mean value of the
* data set using a recurrence relation.
* Source: GNU Scientific Library
*/
virtual Real mean() const;
/**
* Returns the median (e.g. the middle)
* value of the data set.
* This function modifies the
* original data by sorting, so it
* can't be called on const objects.
* Source: GNU Scientific Library
*/
virtual Real median();
/**
* A const version of the median funtion.
* Requires twice the memory of original
* data set but does not change the original.
*/
virtual Real median() const;
/**
* Computes the variance of the data set.
* Uses a recurrence relation to prevent
* data overflow for large sums.
* Note: The variance is equal to the
* standard deviation squared.
* Source: GNU Scientific Library
*/
virtual Real variance() const
{ return this->variance(this->mean()); }
/**
* Computes the variance of the data set
* where the \p mean is provided. This is useful
* for efficiency when you have already calculated
* the mean. Uses a recurrence relation to prevent
* data overflow for large sums.
* Note: The variance is equal to the
* standard deviation squared.
* Source: GNU Scientific Library
*/
virtual Real variance(const Real mean) const;
/**
* Computes the standard deviation of
* the data set, which is simply the square-root
* of the variance.
*/
virtual Real stddev() const
{ return std::sqrt(this->variance()); }
/**
* Computes the standard deviation of
* the data set, which is simply the square-root
* of the variance. This method can be used for
* efficiency when the \p mean has already been computed.
*/
virtual Real stddev(const Real mean) const
{ return std::sqrt(this->variance(mean)); }
/**
* Divides all entries by the largest entry and
* stores the result
*/
void normalize();
/**
* Computes and returns a histogram with n_bins bins for the data
* set. For simplicity, the bins are assumed to be of uniform size.
* Upon return, the bin_members vector will contain unsigned
* integers which give the number of members in each bin.
* WARNING: This non-const function sorts the vector, changing its
* order.
* Source: GNU Scientific Library
*/
virtual void histogram (std::vector<unsigned int>& bin_members,
unsigned int n_bins=10);
/**
* Generates a Matlab/Octave style file which can be used to
* make a plot of the histogram having the desired number of bins.
* Uses the histogram(...) function in this class
* WARNING: The histogram(...) function is non-const, and changes
* the order of the vector.
*/
void plot_histogram(const std::string& filename,
unsigned int n_bins);
/**
* A const version of the histogram function.
*/
virtual void histogram (std::vector<unsigned int>& bin_members,
unsigned int n_bins=10) const;
/**
* Returns a vector of unsigned ints which correspond
* to the indices of every member of the data set
* below the cutoff value "cut".
*/
virtual std::vector<unsigned int> cut_below(Real cut) const;
/**
* Returns a vector of unsigned ints which correspond
* to the indices of every member of the data set
* above the cutoff value cut. I chose not to combine
* these two functions since the interface is cleaner
* with one passed parameter instead of two.
*/
virtual std::vector<unsigned int> cut_above(Real cut) const;
private:
};
} // namespace libMesh
#endif
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