.. _program_listing_file__tmp_ws_src_marti_common_swri_math_util_include_swri_math_util_stat_buffer.h:

Program Listing for File stat_buffer.h
======================================

|exhale_lsh| :ref:`Return to documentation for file <file__tmp_ws_src_marti_common_swri_math_util_include_swri_math_util_stat_buffer.h>` (``/tmp/ws/src/marti_common/swri_math_util/include/swri_math_util/stat_buffer.h``)

.. |exhale_lsh| unicode:: U+021B0 .. UPWARDS ARROW WITH TIP LEFTWARDS

.. code-block:: cpp

   // *****************************************************************************
   //
   // Copyright (c) 2014, Southwest Research Institute® (SwRI®)
   // All rights reserved.
   //
   // Redistribution and use in source and binary forms, with or without
   // modification, are permitted provided that the following conditions are met:
   //     * Redistributions of source code must retain the above copyright
   //       notice, this list of conditions and the following disclaimer.
   //     * Redistributions in binary form must reproduce the above copyright
   //       notice, this list of conditions and the following disclaimer in the
   //       documentation and/or other materials provided with the distribution.
   //     * Neither the name of Southwest Research Institute® (SwRI®) nor the
   //       names of its contributors may be used to endorse or promote products
   //       derived from this software without specific prior written permission.
   //
   // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
   // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
   // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
   // ARE DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> BE LIABLE FOR ANY
   // DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
   // (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
   // LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
   // ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
   // (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
   // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
   //
   // *****************************************************************************
   
   #ifndef MATH_UTIL_STAT_BUFFER_H_
   #define MATH_UTIL_STAT_BUFFER_H_
   
   #ifndef PI
   #define PI 3.14159265358979
   #endif
   
   #include <cmath>
   #include <algorithm>
   
   #include <swri_math_util/generic_ring_buffer.h>
   
   namespace swri_math_util
   {
     template <class T>
     class StatBuffer: public GenRingBuffer<T>
     {
     public:
       void modifyBufferSize(int NumElements)  // moved from private 04/02/2008 JJC
       {
         this->realloc_mem(NumElements);
       }
   
       StatBuffer()
       {
         this->modifyBufferSize(30);
       }
   
       explicit StatBuffer(int NumElements)
       {
         this->modifyBufferSize(NumElements);
       }
   
       ~StatBuffer()
       {
         this->modifyBufferSize(0);
       }
   
       bool UpdateStats()
       {
         return this->computeStats();
       }
   
       bool UpdateDiffStats()
       {
         return this->computeDiffStats();
       }
   
       T reportDiffMean()
       {
         return RetainedDiffStats.mean;
       }
   
       T reportDiffMedian()
       {
         return RetainedDiffStats.median;
       }
   
       T reportDiffMin()
       {
         return RetainedDiffStats.min;
       }
   
       T reportDiffMax()
       {
         return RetainedDiffStats.max;
       }
   
       T reportMean()
       {
         return RetainedStats.mean;
       }
   
       // Computes the mean of the last NumToAvg items in the buffer
       T reportPartialMean(int NumToAvg)
       {
         return this->computeMean(NumToAvg);
       }
   
       T reportMedian()
       {
         return RetainedStats.median;
       }
   
       T reportMin()
       {
         return RetainedStats.min;
       }
   
       T reportMax()
       {
         return RetainedStats.max;
       }
       T reportStd()
       {
         return RetainedStats.std;
       }
   
       T reportVar()
       {
         return RetainedStats.variance;
       }
       T reportRetainedStats()
       {
         return RetainedStats;
       }
   
     private:
       typedef struct
       {
         T mean;
         T min;
         T max;
         T median;
         T std;
         T variance;
       } StatPack;
   
       StatPack RetainedStats;
       StatPack RetainedDiffStats;
   
       T computeMean(int NumToAvg)
       {
         int NumElems = this->size();
         if (NumElems <= 0) return (T)(0.0);
         T CurVal = *this->getTail(0);
         T sum = 0;
         NumToAvg = std::min(NumToAvg, NumElems);
         for (int i = 0; i < NumToAvg; ++i)
         {
           CurVal = *this->getTail(i);
           sum += CurVal;
         }
         T mean = sum/((T)NumToAvg);
         return mean;
       }
   
       bool computeStats()
       {
         int NumElems = this->size();
         if (NumElems <= 0) return false;
   
         T sum = 0;
         T &min = RetainedStats.min;
         T &max = RetainedStats.max;
         T &mean = RetainedStats.mean;
         T &median = RetainedStats.median;
         T &std = RetainedStats.std;
         T &var = RetainedStats.variance;
   
         T CurVal = *this->get(0);
         sum += CurVal;
         min = CurVal;
         max = CurVal;
         mean = CurVal;
         median = CurVal;
         std = 0;
         var = std*std;
   
         // compute mean, min and max
         for (int i = 1; i < NumElems; i++)
         {
           CurVal = *this->get(i);
           sum += CurVal;
           if (CurVal > max) max = CurVal;
           else if (CurVal < min) min = CurVal;
         }
         mean = sum/((T)NumElems);
         sum = 0;
   
         // compute
         if (NumElems > 1)
         {
           T *vec1 = new T[NumElems];  // for median calculation
           for (int i = 0; i < NumElems; i++)
           {
             CurVal = *this->get(i);
             sum += (CurVal-mean)*(CurVal-mean);
             vec1[i] = CurVal;  // for median calculation
           }
           std=(T)sqrt(static_cast<double>(sum/(NumElems-1)));
           var = std*std;
   
           // Compute Median
           std::sort(vec1, vec1+NumElems);  // first sort the data
           if (NumElems % 2 == 0)
           {
             median = (vec1[NumElems/2-1] + vec1[NumElems/2])/2;
           }
           else
           {
             median = vec1[NumElems/2];
           }
           if (NumElems <= 1)
           {
             delete vec1;
           }
           else
           {
             delete [] vec1;
           }
         }
   
         return true;
       }
   
       bool computeDiffStats()
       {
         int NumElems = this->size();
         if (NumElems <= 1) return false;
   
         T sum    = 0;
         T &min    = RetainedDiffStats.min;
         T &max    = RetainedDiffStats.max;
         T &mean    = RetainedDiffStats.mean;
         T &median  = RetainedDiffStats.median;
   
         T *vec1 = new T[NumElems];
   
         T CurVal1 = *this->get(0);
         T CurVal2 = *this->get(1);
         T CVDiff = CurVal2-CurVal1;
   
         vec1[0] = CVDiff;
   
         sum += CVDiff;
         min = CVDiff;
         max = CVDiff;
         mean = CVDiff;
         median = CVDiff;
         for (int i = 1; i < NumElems-1; i++)
         {
           CurVal1 = *this->get(i);
           CurVal2 = *this->get(i+1);
           CVDiff = CurVal2-CurVal1;
           vec1[i] = CVDiff;
           sum += CVDiff;
           if (CVDiff > max) max = CVDiff;
           else if (CVDiff < min) min = CVDiff;
         }
         mean = sum/((T)NumElems);
   
         NumElems--;  // we put in one fewer than NumElems into the vector
         std::sort(vec1, vec1+NumElems);  // first sort the data
         if (NumElems % 2 == 0)
         {
           median = (vec1[NumElems/2-1] + vec1[NumElems/2])/2;
         }
         else
         {
           median = vec1[NumElems/2];
         }
   
         delete [] vec1;
   
         return true;
       }
     };
   }
   
   
   #endif  // MATH_UTIL_STAT_BUFFER_H_