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[uLib Geometry]

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non working version!

+ adds ProgrammableAccessor
+ renaming of some Image structures ...
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Andrea Rigoni
2014-11-03 10:27:52 +00:00
commit 99e771a223
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src/Math/Accumulator.h Normal file
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/*//////////////////////////////////////////////////////////////////////////////
// CMT Cosmic Muon Tomography project //////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////
Copyright (c) 2014, Universita' degli Studi di Padova, INFN sez. di Padova
All rights reserved
Authors: Andrea Rigoni Garola < andrea.rigoni@pd.infn.it >
------------------------------------------------------------------
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 3.0 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.
//////////////////////////////////////////////////////////////////////////////*/
#ifndef U_DATABINNING_H
#define U_DATABINNING_H
#include "Core/Vector.h"
#include "Dense.h"
namespace uLib {
// TODO: USE BOOST ACCUMULATORS //
template <typename T>
class Accumulator_Mean {
typedef std::pair<T,unsigned long> Tmean;
public:
Accumulator_Mean() {
m_Means.push_back( Tmean(0,0) );
}
void operator()(const T data) {
T tmp = 0;
// for(typename std::vector<Tmean>::iterator it = m_Means.begin(); it < m_Means.back(); ++it)
// tmp += it->first/it->second;
for(int i=0; i<m_Means.size()-1;++i)
tmp += m_Means[i].first / m_Means[i].second;
m_Means.back().first += data - tmp;
m_Means.back().second += 1;
}
T operator()() const {
T mean = 0;
// for(typename std::vector<Tmean>::iterator it = m_Means.begin(); it < m_Means.end(); ++it) {
// mean += it->first/it->second; }
for(int i=0; i<m_Means.size();++i)
mean += m_Means[i].first / m_Means[i].second;
return mean;
}
void AddPass() { m_Means.push_back( Tmean(0,0) ); }
private:
Vector< Tmean > m_Means;
};
////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////
// accumulator Trim //
template < typename T, int subsample_size=200 >
class Accumulator_ABTrim {
public:
Accumulator_ABTrim() :
m_Avg(0),
m_InternalCount(0),
m_SizeA(0),
m_SizeB(0),
m_IdA(0),
m_IdB(0)
{}
Accumulator_ABTrim(const Accumulator_ABTrim &c) {
# pragma omp critical
{
m_Avg = c.m_Avg;
m_InternalCount = c.m_InternalCount;
m_SizeA = c.m_SizeA;
m_SizeB = c.m_SizeB;
m_IdA = c.m_IdA;
m_IdB = c.m_IdB;
memcpy (m_Av, c.m_Av, sizeof (m_Av));
}
}
void operator += (T value) {
if(m_InternalCount > subsample_size) {
// array complete and counter over subsample //
if( m_SizeA > 0 && value < m_ValA ) {
;// m_Avg += m_ValA;
}
else if (m_SizeB > 0 && value > m_ValB)
{
;// m_Avg += m_ValB;
}
else
{
m_Avg += value;
m_InternalCount++;
}
}
else if(m_InternalCount >=0) {
// array complete
if(m_SizeA > 0 && value < m_ValA)
{
m_Avg += m_ValA;
m_Av[m_IdA] = value;
for (unsigned int i=0; i < m_SizeA; i++)
if(m_Av[i] > m_Av[m_IdA])
{ m_IdA = i; m_ValA = m_Av[i]; }
}
else if(m_SizeB > 0 && value > m_ValB)
{
m_Avg += m_ValB;
m_Av[m_IdB] = value;
for (unsigned int i=m_SizeA; i < m_SizeA+m_SizeB; i++)
if(m_Av[i] < m_Av[m_IdB])
{ m_IdB = i; m_ValB = m_Av[i]; }
}
else {
m_Avg += value;
}
m_InternalCount++;
}
else { // m_InternalCount < 0
// array is not fullfilled
m_Av[m_SizeA+m_SizeB+m_InternalCount] = value;
m_InternalCount++;
if(m_InternalCount == 0) {
std::sort(m_Av,m_Av+m_SizeA+m_SizeB);
if(m_SizeA > 0) {
m_IdA = m_SizeA-1;
m_ValA = m_Av[m_IdA];
}
if(m_SizeB > 0) {
m_IdB = m_SizeA;
m_ValB = m_Av[m_SizeA];
}
}
}
}
T operator()() {
if(m_InternalCount <= 0) {
std::sort(m_Av, m_Av+m_SizeA+m_SizeB+m_InternalCount);
return m_Av[ (m_SizeA+m_SizeB+m_InternalCount) / 2]; // median value //
}
else {
// return (m_Avg + m_ValA * m_SizeA + m_ValB * m_SizeB) /
// (m_InternalCount + m_SizeA + m_SizeB);
return (m_Avg) / m_InternalCount;
}
}
void SetABTrim(int a, int b) {
if(a+b > subsample_size/2) {
m_SizeA = a/(a+b) * subsample_size/2;
m_SizeB = b/(a+b) * subsample_size/2;
}
else {
m_SizeA = a;
m_SizeB = b;
}
m_Avg = 0;
m_InternalCount = -m_SizeA-m_SizeB;
}
private:
T m_Av[subsample_size/2];
T m_Avg, m_ValA, m_ValB;
int m_IdA, m_IdB, m_InternalCount;
int m_SizeA, m_SizeB;
};
////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////
////////////////////////////////////////////////////////////////////////////////
// Clip Accumulator //
template < typename T, int subsample_size=200 >
class Accumulator_ABClip {
public:
Accumulator_ABClip() :
m_Avg(0),
m_InternalCount(0),
m_SizeA(0),
m_SizeB(0),
m_IdA(0),
m_IdB(0)
{}
Accumulator_ABClip(const Accumulator_ABClip &c) {
# pragma omp critical
{
m_Avg = c.m_Avg;
m_InternalCount = c.m_InternalCount;
m_SizeA = c.m_SizeA;
m_SizeB = c.m_SizeB;
m_IdA = c.m_IdA;
m_IdB = c.m_IdB;
memcpy (m_Av, c.m_Av, sizeof (m_Av));
}
}
void operator += (T value) {
if(m_InternalCount > subsample_size) {
// array complete and counter over subsample //
if( m_SizeA > 0 && value < m_ValA ) {
m_Avg += m_ValA;
}
else if (m_SizeB > 0 && value > m_ValB) {
m_Avg += m_ValB;
}
else {
m_Avg += value;
}
m_InternalCount++;
}
else if(m_InternalCount >=0) {
// array complete
if(m_SizeA > 0 && value < m_ValA)
{
m_Avg += m_ValA;
m_Av[m_IdA] = value;
for (unsigned int i=0; i < m_SizeA; i++)
if(m_Av[i] > m_Av[m_IdA])
{ m_IdA = i; m_ValA = m_Av[i]; }
}
else if(m_SizeB > 0 && value > m_ValB)
{
m_Avg += m_ValB;
m_Av[m_IdB] = value;
for (unsigned int i=m_SizeA; i < m_SizeA+m_SizeB; i++)
if(m_Av[i] < m_Av[m_IdB])
{ m_IdB = i; m_ValB = m_Av[i]; }
}
else {
m_Avg += value;
}
m_InternalCount++;
}
else { // m_InternalCount < 0
// array is not fullfilled
m_Av[m_SizeA+m_SizeB+m_InternalCount] = value;
m_InternalCount++;
if(m_InternalCount == 0) {
std::sort(m_Av,m_Av+m_SizeA+m_SizeB);
if(m_SizeA > 0) {
m_IdA = m_SizeA-1;
m_ValA = m_Av[m_IdA];
}
if(m_SizeB > 0) {
m_IdB = m_SizeA;
m_ValB = m_Av[m_SizeA];
}
}
}
}
T operator()() {
if(m_InternalCount <= 0) {
std::sort(m_Av, m_Av+m_SizeA+m_SizeB+m_InternalCount);
return m_Av[ (m_SizeA+m_SizeB+m_InternalCount) / 2]; // median value //
}
else {
return (m_Avg + m_ValA * m_SizeA + m_ValB * m_SizeB) /
(m_InternalCount + m_SizeA + m_SizeB);
}
}
void SetABTrim(int a, int b) {
if(a+b > subsample_size/2) {
m_SizeA = a/(a+b) * subsample_size/2;
m_SizeB = b/(a+b) * subsample_size/2;
}
else {
m_SizeA = a;
m_SizeB = b;
}
m_Avg = 0;
m_InternalCount = -m_SizeA-m_SizeB;
}
private:
T m_Av[subsample_size/2];
T m_Avg, m_ValA, m_ValB;
int m_IdA, m_IdB, m_InternalCount;
int m_SizeA, m_SizeB;
};
} // uLib
#endif // U_DATABINNING_H