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/***********************************************/
/**
* @file normalEquationDesignVCE.cpp
*
* @brief Accumulate normals from observation equations.
* With individual weights of each arc by variance component estimation.
* @f[ N = A^TPA,\quad n=A^TPl @f]
* @see NormalEquation
* @see Observation
*
* @author Torsten Mayer-Guerr
* @date 2004-12-10
*
*/
/***********************************************/
#include "base/import.h"
#include "config/config.h"
#include "parallel/parallel.h"
#include "files/fileArcList.h"
#include "classes/observation/observation.h"
#include "classes/normalEquation/normalEquation.h"
#include "classes/normalEquation/normalEquationDesignVCE.h"
/***********************************************/
NormalEquationDesignVCE::NormalEquationDesignVCE(Config &config)
{
try
{
FileName fileNameArcList;
renameDeprecatedConfig(config, "arcList", "inputfileArcList", date2time(2020, 7, 7));
readConfig(config, "observation", observation, Config::MUSTSET, "", "");
readConfig(config, "startIndex", startIndex, Config::DEFAULT, "0", "add this normals at index of total matrix (counting from 0)");
readConfig(config, "inputfileArcList", fileNameArcList, Config::OPTIONAL, "", "to accelerate computation");
if(isCreateSchema(config)) return;
intervals = {0, observation->arcCount()};
if(!fileNameArcList.empty())
{
std::vector<Time> timesInterval;
readFileArcList(fileNameArcList, intervals, timesInterval);
}
iter = 0;
sigma2.resize(observation->arcCount(), 1.0);
}
catch(std::exception &e)
{
GROOPS_RETHROW(e)
}
}
/***********************************************/
void NormalEquationDesignVCE::parameterNames(std::vector<ParameterName> &names) const
{
try
{
std::vector<ParameterName> baseNames;
observation->parameterName(baseNames);
for(UInt i=0; i<baseNames.size(); i++)
if(!names.at(i+startIndex).combine(baseNames.at(i)))
logWarningOnce<<"Parameter names do not match at index "<<i+startIndex<<": '"<<names.at(i+startIndex).str()<<"' != '"<<baseNames.at(i).str()<<"'"<< Log::endl;
}
catch(std::exception &e)
{
GROOPS_RETHROW(e)
}
}
/***********************************************/
void NormalEquationDesignVCE::init(MatrixDistributed &/*normals*/, UInt rhsCount)
{
try
{
if(rhsCount != observation->rightSideCount())
throw(Exception("number of right hand sides must agree ("+rhsCount%"%i != "s+observation->rightSideCount()%"%i)"s));
}
catch(std::exception &e)
{
GROOPS_RETHROW(e)
}
}
/***********************************************/
Bool NormalEquationDesignVCE::addNormalEquation(UInt rhsNo, const const_MatrixSlice &x, const const_MatrixSlice &Wz,
MatrixDistributed &normals, Matrix &n, Vector &lPl, UInt &obsCount)
{
try
{
if(!observation->parameterCount())
return TRUE;
const UInt blockStart = normals.index2block(startIndex);
const UInt blockEnd = normals.index2block(startIndex+observation->parameterCount()-1);
for(UInt i=blockStart; i<=blockEnd; i++)
for(UInt k=i; k<=blockEnd; k++)
{
normals.setBlock(i, k);
normals.N(i,k) = (i==k) ? Matrix(normals.blockSize(i), Matrix::SYMMETRIC) : Matrix(normals.blockSize(i), normals.blockSize(k));
}
// compute observation equations
// -----------------------------
logStatus<<"accumulate normals from observation equations"<<Log::endl;
Vector x0 = x.slice(startIndex, rhsNo, observation->parameterCount(), 1);
Matrix Wz0 = Wz.row(startIndex, observation->parameterCount());
Parallel::forEachInterval(sigma2, intervals, [&](UInt arcNo) -> Double
{
// observation equations
Matrix l, A, B;
observation->observation(arcNo, l, A, B);
if(l.rows()==0)
return 0;
// if equations are orthogonal transformed
// additional residuals appended to l
Matrix l2;
if(l.rows()>A.rows())
{
l2 = l.row(A.rows(), l.rows()-A.rows());
l = l.row(0, A.rows());
}
// eliminate arc related parameters
if(B.size())
eliminationParameter(B,A,l);
// Partial redundancy
// trace(A'A*N^(-1)) = trace(A'A*W^(-1)*W^(-T))
// = z'*W^(-1)*A'*A*W^(-T)*z (MonteCarlo trace estimation)
const Double r = l.rows() + l2.rows() - quadsum(A*Wz0)/sigma2.at(arcNo);
const Double sigma2 = (quadsum(l.column(rhsNo)-A*x0) + quadsum(l2.column(rhsNo)))/r;
// right hand side
// ---------------
matMult(1./sigma2, A.trans(),l, n);
for(UInt i=0; i<l.columns(); i++)
lPl(i) += (quadsum(l.column(i))+quadsum(l2.column(i)))/sigma2;
obsCount += l.rows() + l2.rows();
// accumulate normals
// ------------------
for(UInt i=blockStart; i<=blockEnd; i++)
{
const UInt idxN1 = (normals.blockIndex(i) < startIndex) ? (startIndex-normals.blockIndex(i)) : 0;
const UInt idxA1 = (normals.blockIndex(i) < startIndex) ? 0 : (normals.blockIndex(i)-startIndex);
const UInt cols1 = std::min(normals.blockSize(i)-idxN1, A.columns()-idxA1);
rankKUpdate(1/sigma2, A.column(idxA1, cols1), normals.N(i,i).slice(idxN1, idxN1, cols1, cols1));
for(UInt k=i+1; k<=blockEnd; k++)
{
const UInt idxN2 = (normals.blockIndex(k) < startIndex) ? (startIndex-normals.blockIndex(k)) : 0;
const UInt idxA2 = (normals.blockIndex(k) < startIndex) ? 0 : (normals.blockIndex(k)-startIndex);
const UInt cols2 = std::min(normals.blockSize(k)-idxN1, A.columns()-idxA1);
matMult(1/sigma2, A.column(idxA1, cols1).trans(), A.column(idxA2, cols2), normals.N(i,k).slice(idxN1, idxN2, cols1, cols2));
}
}
return sigma2;
}, normals.communicator());
normals.reduceSum(FALSE);
Parallel::reduceSum(n, 0, normals.communicator());
Parallel::reduceSum(lPl, 0, normals.communicator());
Parallel::reduceSum(obsCount, 0, normals.communicator());
Parallel::broadCast(sigma2, 0, normals.communicator());
return (++iter >= 3); // ready after 2 iterations
}
catch(std::exception &e)
{
GROOPS_RETHROW(e)
}
}
/***********************************************/
Vector NormalEquationDesignVCE::contribution(MatrixDistributed &Cov)
{
try
{
logWarningOnce<<"In NormalEquationDesignVCE: contribution is not implemented"<<Log::endl;
return Vector(Cov.dimension());
}
catch(std::exception &e)
{
GROOPS_RETHROW(e)
}
}
/***********************************************/
|
